University of São Paulo
“Luiz de Queiroz” College of Agriculture
Rotational stocking management on elephant grass for dairy cows: grazing
strategies, animal productivity, enteric methane and nitrous oxide emissions
Guilhermo Francklin de Souza Congio
Thesis presented to obtain the degree of Doctor in
Science. Area: Animal Science and Pastures
Piracicaba
2018
Guilhermo Francklin de Souza Congio
Agricultural Engineer
Rotational stocking management on elephant grass for dairy cows: grazing strategies,
animal productivity, enteric methane and nitrous oxide emissions
Advisor:
Prof. Dr. SILA CARNEIRO DA SILVA
Thesis presented to obtain the degree of Doctor in
Science. Area: Animal Science and Pastures
Piracicaba
2018
2
Dados Internacionais de Catalogação na Publicação
DIVISÃO DE BIBLIOTECA – DIBD/ESALQ/USP
Congio, Guilhermo Francklin de Souza
Rotational stocking management on elephant grass for dairy cows: grazing strategies, animal productivity, enteric methane and nitrous oxide emissions /
Guilhermo Francklin de Souza Congio. - - Piracicaba, 2018.
107 p.
Tese (Doutorado) - - USP / Escola Superior de Agricultura “Luiz de Queiroz”.
1. Gases de efeito estufa 2. Gramínea tropical 3. Interceptação luminosa do dossel 4. Manejo do pastejo 5. Qualidade da forragem I. Título
3
To my parents Francklin Roberto Leite Congio (in memorian) and Neuza Aparecida de Souza Congio,
for their love, support and education
To my sister Ana Carolina de Souza Congio and my nephew ‘Greg’, for their love and support
4
ACKNOWLEDGMENTS
First I would like to express my deepest gratitude to my family; mom, sister and Greg, for
their constant encouragement and understanding. Without your love and support I would have never
made it. This thesis is dedicated to you.
To my advisor, Dr. Sila Carneiro da Silva, for his guidance and support during my doctorate
program. Above the scientific insights that you provided me, thank you for example of professor and
researcher, conducting all activities with professionalism, dedication, and commitment.
Thanks are also due to “Luiz de Queiroz” College of Agriculture – University of São Paulo
and the Department of Animal Science that since my undergraduation provided me an unique
opportunity that changed the course of my life. To faculty staff of the Department of Animal Science
for all training along my journey, specially to Dr. Moacyr Corsi and “Projeto CAPIM” for his
tremendous contribution in my formation.
I would also like to thank my guidance committee, in special to Dr. Marília Barbosa
Chiavegato, for her constant support all the time and for valuable contributions in reviewing and
suggestions for improving the thesis, and Dr. Lilian Elgalise Techio Pereira, for her valuable
suggestions.
To researchers Dr. Alexandre Berndt, Dr. Patrícia Perondi Anchão Oliveira, Dr. Rosa
Toyoko Shiraishi Frighetto, and to Carlos Eduardo Jordão, Dagmar Oliveira, and Melissa Baccan from
EMBRAPA, for their contribution in greenhouse gases sampling and analisys.
To Dr. Pablo Gregorini and Dr. Thomas Maxwell, Faculty of Agricultural and Life Sciences
at Lincoln University. Thank you for receiving me as visiting student, for sharing your research, and
offering valuable comments towards improving the first published manuscript from my thesis.
My appreciation to the undergraduate students who dedicated few or several weeks helping
me in the field and laboratory: Marcel Junqueira Tarraf, Wilton Mourão Filho, João Leonardo Corte
Baptistella, Ana Caroline Amorim Krol, Taís Fernandes Landim, Rafaela Aparecida Moraes, Erik
Yuri Camargo Barros, João Gabriel Costa Dearo, Rosalie Cuillé, and Felipe Leiber Coelho Pimentel.
My special thanks to friends in my second journey at Piracicaba: Anna Fett, Carolina
Aroeira, Eliana Geremia, Fagner Júnior, Guilherme Natsumeda, Guilherme Portes, Larissa Garcia,
Max Pasetti, Otávio Almeida, Patrícia Barbosa, and Pedro Guerreiro. I would also like to thank all
colleagues from “GEPF” and “LAPF” for scientific discussions and fun moments, and to graduate
coleagues for fun moments.
Finally, to the São Paulo State Research Foundation (FAPESP) for funding the project
(Process nº 2016/22040-2), and to CNPq and CAPES for providing scholarship during my doctorate
program.
5
CONTENTS
RESUMO ............................................................................................................................................................... 7
ABSTRACT ........................................................................................................................................................... 8
1. INTRODUCTION ....................................................................................................................................... 9
REFERENCES...................................................................................................................................................... 10
2. LITERATURE REVIEW ......................................................................................................................... 15
2.1. GRAZING MANAGEMENT AND HERBAGE CHARACTERISTICS ................................................................ 15
2.2. GRAZING MANAGEMENT AND ANIMAL RESPONSES.............................................................................. 17
2.3. GRAZING MANAGEMENT AND SOIL PROPERTIES .................................................................................. 19
2.4. DIURNAL VARIATION IN HERBAGE CHEMICAL COMPOSITION AND ITS IMPLICATIONS TO PASTURE-
BASED ANIMAL PRODUCTION SYSTEMS .............................................................................................................. 20
2.5. CONCEPTUAL MODEL, OBJECTIVES AND HYPOTHESES ......................................................................... 22
REFERENCES...................................................................................................................................................... 23
3. STRATEGIC GRAZING MANAGEMENT TOWARDS SUSTAINABLE INTENSIFICATION AT
TROPICAL PASTURE-BASED DAIRY SYSTEMS ...................................................................................... 37
ABSTRACT........................................................................................................................................................... 37
3.1. INTRODUCTION ................................................................................................................................... 37
3.2. MATERIAL AND METHODS .................................................................................................................. 39
3.2.1. Study site ....................................................................................................................................... 39
3.2.2. Treatments and experimental design ............................................................................................. 39
3.2.3. Plant measurements ....................................................................................................................... 40
3.2.4. Herd and feeding ........................................................................................................................... 41
3.2.5. Animal measurements ................................................................................................................... 42
3.2.6. Statistical analysis.......................................................................................................................... 43
3.3. RESULTS ............................................................................................................................................. 43
3.3.1. Canopy light interception and sward surface height ...................................................................... 43
3.3.2. Canopy cover ................................................................................................................................. 44
3.3.3. Herbage characteristics .................................................................................................................. 45
3.3.4. Dry matter intake, animal performance and CH4 emissions .......................................................... 46
3.3.5. Milk yield and CH4 emissions per hectare ..................................................................................... 47
3.4. DISCUSSION ........................................................................................................................................ 47
3.5. CONCLUSIONS ..................................................................................................................................... 51
REFERENCES...................................................................................................................................................... 51
4. STRATEGIC GRAZING MANAGEMENT AND NITROUS OXIDE FLUXES FROM PASTURE
SOILS IN TROPICAL DAIRY SYSTEMS ...................................................................................................... 59
ABSTRACT........................................................................................................................................................... 59
4.1. INTRODUCTION ................................................................................................................................... 59
4.2. MATERIAL AND METHODS .................................................................................................................. 61
4.2.1. Study site ....................................................................................................................................... 61
6
4.2.2. Treatments and experimental design .............................................................................................. 61
4.2.3. Soil flux measurements, analysis and flux calculation .................................................................. 62
4.2.4. Weather and ancillary measurements............................................................................................. 63
4.2.5. Statistical analysis .......................................................................................................................... 64
4.3. RESULTS .............................................................................................................................................. 64
4.3.1. Weather conditions ........................................................................................................................ 64
4.3.2. Soil parameters .............................................................................................................................. 65
4.3.3. Nitrous oxide fluxes ....................................................................................................................... 67
4.3.4. Principal component analysis......................................................................................................... 69
4.4. DISCUSSION ......................................................................................................................................... 70
4.5. CONCLUSIONS ..................................................................................................................................... 73
REFERENCES ...................................................................................................................................................... 74
5. EFFECTS OF TIMING OF PADDOCK ALLOCATION ON MILK YIELD AND ENTERIC
METHANE EMISSIONS FROM DAIRY COWS ........................................................................................... 81
ABSTRACT........................................................................................................................................................... 81
5.1. INTRODUCTION .................................................................................................................................... 81
5.2. MATERIAL AND METHODS .................................................................................................................. 82
5.2.1. Study site ....................................................................................................................................... 83
5.2.2. Treatments and experimental design .............................................................................................. 83
5.2.3. Plant measurements ....................................................................................................................... 83
5.2.4. Herd and feeding ............................................................................................................................ 84
5.2.5. Animal measurements .................................................................................................................... 84
5.2.6. Statistical analysis .......................................................................................................................... 86
5.3. RESULTS .............................................................................................................................................. 86
5.3.1. Sward characteristics ..................................................................................................................... 86
5.3.2. Herbage chemical composition ...................................................................................................... 86
5.3.3. Animal performance ...................................................................................................................... 87
5.3.4. Dry matter intake and enteric CH4 emissions ................................................................................ 88
5.4. DISCUSSION ......................................................................................................................................... 89
5.5. CONCLUSIONS ..................................................................................................................................... 91
REFERENCES ...................................................................................................................................................... 92
6. GENERAL CONSIDERATIONS ........................................................................................................... 101
REFERENCES .................................................................................................................................................... 103
7. CONCLUSIONS ...................................................................................................................................... 107
7
RESUMO
Pastejo rotativo em capim-elefante para vacas leiteiras: estratégias de pastejo, produtividade
animal, emissões de metano entérico e de óxido nitroso
Sistemas baseados no uso de pastagens são importantes fornecedores de leite para a
indústria de latícinios e, dessa forma, terão papel relevante para suportar a crescente demanda por
alimentos. No entanto, essa oferta adicional de leite deve ser obtida através de maiores
produtividades resultantes da intensificação de sistemas de produção já existentes por meio de
estratégias ambientalmente seguras e economicamente rentáveis em direção à intensificação
sustentável. A hipótese central deste estudo foi que estratégias simples de manejo do pastejo
podem melhorar a eficiência e, ao mesmo tempo, reduzir os principais impactos ambientais dos
sistemas de produção animal em pastagens tropicais. Foram realizados dois experimentos em
pastagem de capim-elefante (Pennisetum purpureum Schum. Cv. Cameroon) não-irrigada em
Piracicaba, SP, Brasil. O objetivo do primeiro experimento foi avaliar a influência de duas metas
pré-pastejo (95% e máxima interceptação de luz pelo dossel durante a rebrotação; IL95% e ILMáx,
respectivamente) sobre a estrutura do pasto e valor nutritivo da forragem, consumo de matéria seca
(CMS), produção de leite, taxa de lotação, emissões de metano entérico (CH4) de vacas HPB ×
Jersey, e o fluxo de óxido nitroso dos solos. Os resultados indicaram que a altura pré-pastejo foi
maior para ILMáx (≈135 cm) do que IL95% (≈100 cm) e pode ser usada como um guia de campo
confiável para monitorar a estrutura do pasto. O manejo do pastejo com base nos critérios de IL95%
melhorou o valor nutritivo da forragem e a eficiência de pastejo, permitindo maior CMS, produção
de leite e taxa de lotação. A emissão diária de CH4 entérico não foi afetada; no entanto, as vacas
que pastejaram o capim-elefante manejado por IL95% foram mais eficientes e emitiram 21% menos
CH4/kg de leite e 18% menos CH4/kg de MS consumida. O aumento de 51% na produção de leite
por hectare superou o aumento de 29% nas emissões de CH4 entérico por hectare para a meta
IL95%. Os fluxos de óxido nitroso não foram afetados pelas metas pré-pastejo. De maneira geral, o
manejo do pastejo com base na meta IL95% é uma prática ambientalmente segura que melhora a
eficiência de uso dos recursos alocados por meio da otimização de processos envolvendo plantas,
ruminantes e sua interface, e aumenta a eficiência da produção de leite em sistemas baseados em
pastagens tropicais. Uma vez que a meta pré-pastejo ideal foi estabelecida durante o primeiro
experimento (IL95%), a segunda etapa consistiu-se em um refinamento da primeira. O segundo
objetivo foi descrever e medir a influência de dois horários de alocação de novos piquetes aos
animais (AM e PM) sobre a composição química da forragem, CMS, produção e composição do
leite, e emissões de CH4 entérico de vacas HPB × Jersey. Os resultados confirmaram a
compreensão geral da variação diurna na composição química da forragem em direção a maiores
concentrações de matéria seca e de carboidratos não-fibrosos, e menor concentração de
componentes da fibra na forragem amostrada pela à tarde. No entanto, o maior valor nutritivo da
forragem da tarde não aumentou o CMS e a produção de leite, nem diminuiu a intensidade de
emissão de CH4 das vacas leiteiras. Os resultados também indicaram que a alocação à tarde pode
ser uma estratégia de manejo simples e útil que resulta em maior partição de N para produção de
proteína, e menor excreção de N ureico no leite. A associação da meta pré-pastejo IL95% e a
alocação do rebanho para um novo piquete à tarde poderia trazer benefícios econômicos,
produtivos e ambientais para a intensificação sustentável de sistemas baseados em pastagens
tropicais.
Palavras-chave: Gases de efeito estufa; Gramínea tropical; Interceptação luminosa do dossel;
Manejo do pastejo; Qualidade da forragem
8
ABSTRACT
Rotational stocking management on elephant grass for dairy cows: grazing strategies, animal
productivity, enteric methane and nitrous oxide emissions
Pasture-based systems are important milk suppliers to dairy industry and thereby will
play relevant role to support the growing demand for food. However, this additional milk supply
must be obtained through higher yields resulting from intensification of existing farming systems
through strategies environmentally friendly and economically profitable towards sustainable
intensification. The central hypothesis of this study was that simple grazing management strategies
can improve the efficiency while reduce the key environmental issues of tropical pasture-based
dairy systems. Two experiments were carried out on a rainfed and non-irrigated elephant grass
(Pennisetum purpureum Schum. cv. Cameroon) pasture in Piracicaba, SP, Brazil. The objective of
the first experiment was to investigate the influence of two pre-grazing targets (95% and
maximum canopy light interception during pasture regrowth; LI95% and LIMax, respectively) on
sward structure and herbage nutritive value, dry matter intake (DMI), milk yield, stocking rate,
enteric methane (CH4) emissions by Holstein × Jersey dairy cows, and nitrous oxide fluxes from
the soil. Results indicated that pre-grazing canopy height was greater for LIMax (≈135 cm) than
LI95% (≈100 cm) and can be used as a reliable field guide for monitoring sward structure. Grazing
management based on the LI95% target improved herbage nutritive value and grazing efficiency,
allowing greater DMI, milk yield and stocking rate by dairy cows. Daily enteric CH4 emission was
not affected; however, cows grazing elephant grass at LI95% were more efficient and emitted 21%
less CH4/kg of milk yield and 18% less CH4/kg of DMI. The 51% increase in milk yield per
hectare overcame the 29% increase in enteric CH4 emissions per hectare for the LI95% target.
Nitrous oxide fluxes were not affected by pre-grazing targets. Overall, strategic grazing
management is an environmentally friendly practice that improves the use efficiency of allocated
resources through optimization of processes involving plant, ruminant and their interface, and
enhances milk production efficiency of tropical pasture-based systems. Once the ideal pre-grazing
target was established during he first experiment (LI95%), the second step consisted of a refinement
of the first phase. The second objective was to describe and measure the influence of two timings
of new paddock allocation to cows (AM and PM) on herbage chemical composition and DMI,
milk yield, milk compostion, and enteric CH4 emissions of Holstein × Jersey dairy cows. Results
supported the general understanding of diurnal variation in herbage chemical composition towards
greater concentrations of dry matter and non-fibrous carbohydrates, and lower concentration of
fiber components in the afternoon herbage. However, the higher nutritive value of the afternoon
herbage did not result in increasead DMI and milk yield, or decreased intensity of CH4 emission
by dairy cows. Our findings also indicate that new paddock allocation in the afternoon can be a
simple and useful grazing strategy that results in greater N partitioning to protein yield, and lower
excretion of urea N in milk. The association of LI95% pre-grazing target and PM allocation could
bring economic, productive and environmental benefits towards sustainable intensification of
tropical pasture-based systems.
Keywords: Greenhouse gases; Tropical grass; Canopy light interception; Grazing management;
Herbage quality
9
1. INTRODUCTION
To meet the world's future food demand, agricultural outputs must grow from 60 to 120% by
2050 (Godfray et al., 2010; Conforti, 2011; Alexandratos and Bruinsma, 2012) while agriculture
environmental footprint must decrease dramatically (Foley et al., 2011). Therefore, food producers are
faced with the challenge of supplying food demand through environmentally friendly (Tilman et al.,
2002) and economic favorable practices (Foote et al., 2015; Gregorini et al., 2017). In developing
countries, agriculture production must increase 80% through higher yields resulting from
intensification of existing agricultural systems (Conforti, 2011). Sustainable intensification was
defined as a form of production wherein yields are increased without adverse environmental impact
and without the cultivation of more land (Royal Society, 2009). Despite contested (Struik and Kuyper,
2017), this term was deeply discussed (Pretty and Bharucha, 2014) and highlights the needs to
increase the productivity (i.e. agricultural product outputs per hectare) of current agricultural systems
through practices that minimize key environmental issues (Garnett and Godfray, 2012).
Global warming observed since the mid-20th century is mostly attributed to anthropic
activities that emit greenhouse gases (GHG; IPCC, 2014). Agricultural systems contribute with 10-
12% of global estimated GHG emissions, 50% of methane (CH4) and 60% of nitrous oxide (N2O)
from anthropogenic sources (Smith et al., 2007). Dairy farming systems provide essential high-quality
protein that is a major component of human diet (O’Brien et al., 2015; Aguirre-Villegas et al., 2017).
However, considering livestock production, they are the second largest contributor accounting for 20%
of total GHG emissions (Gerber et al., 2013). Life cycle assessment approaches reported enteric CH4
and N2O from soils as predominant sources of GHG in dairy farming systems, representing
approximately 90% of total GHG emissions (Aguirre-Villegas et al., 2017). In tropical dairy farming
systems, Cunha et al. (2016) reported 53% for enteric CH4 and 18% for N2O of total GHG emissions
for typical Brazilian dairy farms.
Pasture-based systems are important milk suppliers to dairy industry in temperate (Chapman,
2016; Macdonald et al., 2017) and tropical climate (Santos et al., 2014) and thereby will play relevant
role to support growing demand (Godfray et al., 2010; Conforti, 2011; Alexandratos and Bruinsma,
2012). The intensification of temperate pasture-based dairy systems has been associated with
increasing inputs such as nitrogen fertilizer or imported supplements (Beukes et al., 2012; Foote et al.,
2015; Macdonald et al., 2017). However, such intensification practices are associated with issues of
environmental concern, namely increased GHG emissions and water degradation (Foley et al., 2011;
Vogeler et al., 2013; Foote et al., 2015). Alternatively, grazing management strategies that optimize
herbage utilization and digestible dry matter intake by grazing cows could improve land-use and
decrease GHG emissions of pasture-based dairy systems (Muñoz et al., 2016; Gregorini et al., 2017).
The key to understanding the principles of grazing management strategies is to comprehend
that the harvestable components are photosynthetic organs – predominantly leaves (Parsons et al.,
10
2011). Studies have reported that grazing management strategies that prioritize leaf accumulation
rather than other plant-part components may be useful tools towards efficient pasture-based systems in
the tropics (Silveira et al., 2013; Pereira et al., 2014; Da Silva et al., 2015; Da Silva et al., 2017;
Sbrissia et al., 2018). Leafy swards mean high herbage quality, since it provides high short-term intake
rate by grazing animals, as leaves require less strength to be harvested, and also because they have
greater nutritive value than stems and dead material (Trindade et al., 2007; Silva, 2017). In this sense,
the development of efficient pasture-based systems with perennial tropical grasses usually focuses on
the control of stem elongation and excessive senescence and dead material accumulation by grazing
management strategies (Da Silva and Carvalho, 2005; Da Silva et al., 2015).
Although the studies aforementioned have demonstrated the benefits of grazing management
strategies, most focused solely on plant responses. There is a knowledge gap relating plant and animal
responses and environmental benefits in tropical pasture-based dairy systems. Therefore, the central
objective of this study was to investigate the influence of simple grazing management strategies and
their effects on the relationships among plant, animal and soil components. The central hypothesis was
that simple grazing management strategies optimize processes inherent to plant growth, plant-animal
interface, and animal, and provide environmental services, improving efficiency of tropical pasture-
based system.
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144–164. http://dairy.ifas.ufl.edu/rns/2014/santos.pdf.
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Sbrissia, A.F., Duchini, P.G., Zanini, G.D., Santos, G.T., Padilha, D.A., Schimitt, D., 2018.
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15
2. LITERATURE REVIEW
2.1. Grazing management and herbage characteristics
The pasture management concept involves a wide range of aspects such as the choice of the
ideal forage species or mix, liming, nutrient balance and fertilization rate, weed and pest management,
soil conservation practices, paddock subdivision, watering system, type and level of supplementation,
among others. On the other hand, grazing management is a specific term that refers to monitoring the
sward state and controlling the grazing process by grazers through targets that optimize herbage
regrowth and animal responses (Da Silva and Corsi, 2003). In continuous stocking grazing
management strategies, the question would be at which sward surface height (SSH) the grazer should
keep the herbage in order to balance sub-optimal plant and animal responses? In intermittent grazing
management strategies (i.e. rotational grazing) the question would be which are the most adequate pre-
and post-grazing heights to achieve the same goals?
Rotational stocking management is widely used in temperate grazing systems and is also
being adopted in tropical conditions mainly in dairy farming systems (Santos et al., 2014; Chapman et
al., 2016). A large number of studies have been developed to try and understand the most adequate
combination between frequency and severity of defoliation for several tropical forage species, or the
ideal pre- and post-grazing heights (i.e. frequency and severity, respectively) (Carnevalli et al., 2006;
Barbosa et al., 2007; Trindade et al., 2007; Da Silva et al., 2009; Difante et al., 2009a; Difante et al.,
2009b; Giacomoni et al., 2009; Difante et al., 2010; Barbosa et al., 2011; Gimenes et al., 2011; Zanini
et al., 2012; Silveira et al., 2013; Geremia et al., 2014; Pereira et al., 2014; Pereira et al., 2015a;
Pereira et al., 2015b; Silveira et al., 2016; Da Silva et al., 2017; Pereira et al., 2018). The majority of
these studies evaluated frequencies based on canopy light interception (LI) combined with severities
based in fixed post-grazing heights, and focused on plant responses such as tillering dynamics,
morphogenesis, organic reserves, herbage nutritive value, sward structure, and herbage accumulation.
These studies observed that tropical grasses regrowth is a function of canopy LI and leaf area index
(LAI) with accumulation of herbage fitted to a sigmoid curve with three distinct phases as proposed
for temperate swards by Brougham (1955). During the early stages of regrowth, leaves are the main
morphological component accumulated. As LAI increases, canopy light intra-competition increases
and plants change their growth pattern as a means of optimizing light capture through stem elongation.
The shift in growth pattern occurs when canopy LI reaches and exceeds 95% (LI95%; Da Silva et al.,
2015). These studies have shown systematic relationship between SSH and LI, establishing SSH as a
reliable field index for monitoring and controlling herbage regrowth (Da Silva et al., 2015).
Grazing management affects the distribution and arrangement of above-ground plant-part
components (i.e. sward structure, Laca and Lemaire, 2000). The frequency of defoliation based on
LI95% often minimizes stem elongation of tropical forage, maximizing leaf blade proportion over
16
others sward plant-part components (Carnevalli et al., 2006; Barbosa et al., 2007; Trindade et al.,
2007; Da Silva et al., 2009). Studies showed that swards managed with the LI95% target have greater
leaf appearance rate, leaf elongation rate, and leaf accumulation in successive grazing cycles than
swards managed with the LIMax target, which have greater stem elongation and senescence (Barbosa et
al., 2011; Pereira et al., 2014; Pereira et al., 2015b; Silveira et al., 2016; Pereira et al., 2018). As a
result between leaf growth and senescence rates, LI95% provides greater average net growth rate and is
considered the critical LAI to interrupt regrowth under rotational grazing management (Da Silva et al.,
2015). Pereira et al. (2015a) also reported changes in horizontal sward structure as a function of
grazing management. The LI95% target provided greater soil cover by elephant grass tussocks
(Pennisetum purpureum Schum. cv. Napier). Furthermore, the exacerbated competition for light for
the LIMax target resulted in tiller death, reduced tillering, and less stability of plant population
impairing pasture persistence (Pereira et al., 2015a).
Herbage chemical composition is a function of the proportion of plant-part components in
the herbage mass and their tissue anatomy (Moore, 1994). Stems contain higher proportion of cell wall
tissues and less photosynthetic tissues than leaves (Wilson and Kennedy, 1996). On the other hand,
most protein compounds are present in leaves, with the majority associated with photosynthetic
enzymes (Gastal and Durand, 2000). As a consequence of changes in plant-part components in the
grazing strata, the frequency of defoliation associated with the LI95% target is an efficient tool to
improve herbage nutritive value in tropical grasses (Trindade et al., 2007). Studies reported lower
acid-detergent fiber and greater crude protein concentrations for elephant grass (Pennisetum
purpureum Schum) (Voltolini et al., 2010a; Geremia et al., 2014), and greater in vitro digestible
organic matter for signal grass (Brachiaria decumbens cv. Basilisk, syn. Urochloa decumbens Stapf R.
D. Webster) (Pedreira et al., 2017) managed with the LI95% rather than the LIMax target.
Efficient pasture-based systems should maximize the proportion of consumed relatively to
produced herbage (Chapman et al., 2016). In order to do that, they have to prioritize leaf accumulation
and increase grazing efficiency or herbage utilization through reduced losses by cattle trampling and
plant senescence (Da Silva et al., 2015; Chapman et al., 2016). Several studies have shown greater
senescence for tropical grasses managed with the LIMax compared to the LI95% target because of the
longer regrowth intervals (Barbosa et al., 2011; Pereira et al., 2014; Pereira et al., 2015b; Silveira et
al., 2016; Pereira et al., 2018). Longer regrowth intervals usually result in taller swards with high pre-
grazing herbage mass (Da Silva et al., 2009; Pereira et al., 2015b) which are more susceptible to losses
by cattle trampling (Carnevalli et al., 2006; Silveira et al., 2013). Both greater senescence and grazing
losses by cattle trampling contribute to dereased grazing efficiency of taller swards managed with the
LIMax compared to the LI95% target.
Studies showing the numerous benefits on plant growth managed with the LI95% target were
mostly compared with management using the LIMax target (early known as LI100%). Recently, Sbrissia
et al. (2018) assessing a range of LI targets lower than 95%, highlighted a new opportunity for tropical
17
forage grasses under rotational stocking management. They suggested that there is a range of pre-
grazing heights with no impact on net herbage accumulation rate, as long as the defoliation used is
moderate (removal of no more than 50% of the initial pre-grazing height). The authors explained that
the same homeostatic mechanisms that buffer herbage accumulation across a range of targets in
continuously stocked swards can be applied to rotationally stocked swards. If more studies corroborate
these responses for different tropical grasses, farmers would have a flexible optimal range to manage
their pastures where LI95% would be the upper threshold to interrupt sward regrowth.
Regarding severity of defoliation, studies that assessed mainly plant responses based on
fixed residual post-grazing heights usually observed that greater severities (i.e. lower post-grazing
heights) were positively related to herbage accumulation and grazing efficiency, and negatively related
to nutritive value of the consumed herbage (Carnevalli et al., 2006; Barbosa et al., 2007; Difante et al.,
2009b). However, using the concept of severity of defoliation as a percentage of initial pre-grazing
height and having the grazing animal under perspective, studies have shown that levels of defoliation
until 40-50% of the pre-grazing height result in relatively stable and high rate of short-term herbage
intake (Fonseca et al., 2012; Fonseca et al., 2013; Carvalho, 2013; Mezzalira et al., 2014). They
reported that beyond this herbage depletion level preferred leaves become scarce and stem and dead
material become predominant in succeeding lower pasture layers impairing the efficiency of nutrient
harvesting per unit of bite (Carvalho, 2013). According to Zanini et al. (2012), regardless of forage
species and pre-grazing height, 90% of stem is present in the lower half of the canopy.
2.2. Grazing management and animal responses
Defoliation strategies change tissue turnover, photosynthates allocation pattern, and finally
the rate of processes related to morphogenetic characteristics that, in turn, determine sward structural
characteristics (Chapman and Lemaire, 1993). As detailed in the previous session, the pre-grazing
target of LI95% under rotational grazing management optimizes harvestable plant-part components (i.e.
leaves) rather than support morphological components (i.e. stems) and dead material, which are plant-
part components avoided by grazers (Trindade et al., 2007). Furthermore, grazing losses by cattle
trampling are reduced with grazing at LI95% compared to LIMax. As a result of greater leaf
accumulation and lower losses by trampling and senescence, the LI95% target provides more feed per
hectare supporting higher stocking rates. Voltolini et al. (2010b) and Gimenes et al. (2011) found
stocking rate increases ranging from 10% to 42% in elephant and palisade grass pastures managed
with the LI95% relative to the LIMax target.
Daily herbage intake is determined by interactions between sward structure and grazing
animals (Wade and Carvalho, 2000). Poppi et al. (1987) suggested that herbage intake follows an
asymptotic distribution represented by two distinct phases. In the first ascending phase, herbage intake
18
is related to sward structure (i.e. herbage or leaf mass, pre-SSH, leaf-to-stem ratio) and grazing
behavior (i.e. grazing time, diet selection, bite mass and bite rate), which are characteristics strongly
affected by grazing management strategies (Da Silva and Carvalho, 2005). In the second asymptotic
phase, nutritional factors such as herbage chemical composition, digesta retention time in the rumen
and concentration of metabolic compounds are more relevant in controlling intake (Poppi et al., 1987).
Swards constantly kept at taller heights (such as those managed with the LIMax target) result in lower
short-term intake rate owing to the excessive length of leaf blade and lower bulk density of herbage in
the upper strata (Palhano et al., 2007; Fonseca et al., 2013; Carvalho, 2013). At the rumen level, more
fibrous herbage (i.e. higher NDF, ADF and lignin) is associated with greater ruminal retention time,
lower fermentation and passage rate, and lower herbage intake (Mertens, 1994; Allen, 1996; Allen,
2000; Forbes, 2007). On the other hand, leafy swards with high herbage nutritive value as those
resulting from management with the LI95% target would optimize animal grazing behavior and rumen
fill in order to achieve high daily herbage intake. It is worth mentioning that frequencies of defoliation
based on fixed-length rest periods in an attempt to easy operationalize the herd management into set-
paddock area are unable to adequately control sward structure and usually result in decreased animal
performance and animal productivity (Pedreira et al., 2009; Voltolini et al., 2010b; Euclides et al.,
2014).
The severity of defoliation can also affect grazing behavior and nutritive value of the
consumed herbage (Difante et al., 2009b; Fonseca et al., 2012). Fonseca et al. (2012) reported that
severities of defoliation greater than 40-50% removal of pre-grazing height resulted in linear decrease
of the short-term rate of herbage intake jeopardizing daily herbage intake and animal performance. At
the same time, severities of defoliation greater than the ones proposed by Fonseca et al. (2012) would
optimize grazing efficiency and stocking rate (Difante et al., 2009a). Thus, there is a clear trade-off
between animal performance and stocking rate as proposed early by Mott (1960), and the most
productive grazing strategy should be one able to conciliate significant levels of animal performance
with the highest possible stocking rate. Recent approaches have shown that defoliation levels around
45% of pre-grazing height can increase in 68% animal performance coupled with stocking rate
reductions of around 30% (Euclides et al., 2015; Euclides et al., 2018). Therefore, grazing
management strategies that associate the LI95% pre-grazing target with moderate levels of defoliation
(not exceeding the removal of 50% of the pre-grazing height) seem to be more appropriate to achieve
higher levels of animal productivity.
However, at the present time, environmental concern is undividable from successful and
productive animal production systems (Chiavegato et al., 2018). Greenhouse gases (GHG) emissions
are estimated to be the most significant among all categories of environmental impacts in livestock
farming systems (O’Brien et al., 2012; Guerci et al., 2013; Gregorini et al, 2016), and enteric methane
(CH4) represents more than 80% of total GHG emissions in pasture-based dairy farming systems
(Aguirre-Villegas et al., 2017). Enteric CH4 production from animal digestion is affected by the
19
amount and nature of feed, and the extent of its degradation, which in turn determines the amount of
hydrogen formed in the rumen (Janssen, 2010). The model proposed by Janssen (2010) suggests that
greater digesta passage rates increase hydrogen concentration in the rumen. Consequently,
microorganisms would select pathways thermodynamically more favorable to this condition, which
produce less hydrogen resulting in less CH4 formed per unit of feed ingested (i.e. CH4 yield). Studies
carried out in temperate grazing systems have shown that pre-grazing height of typical ryegrass ×
white clover mixed pastures can be an important tool to mitigate enteric CH4 emissions from pasture-
based farming systems. These studies reported no differences in daily enteric CH4 emissions from beef
heifers (Boland et al., 2013) and dairy cows (Wims et al., 2010; Muñoz et al., 2016) grazing low
versus high herbage mass swards, even with significant differences reported in daily herbage intake
and herbage nutritive value. However, they observed reductions on CH4 yield and CH4 emission
intensity (i.e CH4 per unit of final product) from cows grazing low versus high herbage mass swards
(Wims et al., 2010; Boland et al., 2013; Muñoz et al., 2016).
2.3. Grazing management and soil properties
Grazing management strategies can strongly affect processes related to plant growth (Da
Silva et al., 2015), animal ingestive behavior (Da Silva and Carvalho, 2005), and soil characteristics
(de Klein et al., 2008; Luo et al., 2017). Frequency of defoliation based on the LI95% target increases
leaf accumulation and reduces litter deposition owing to decreased senescence and grazing losses by
cattle trampling, compared with the LIMax target. As a consequence, studies have shown that the LI95%
target provides more feed per hectare supporting up to 42% increase in stocking rate (Voltolini et al.,
2010b). Higher stocking rates modify soil properties (i.e. bulk density, moisture, temperature, pH,
aeration) (Warren et al., 1986; Silva et al., 2003; Schmalz et al., 2013) and increase nitrogen (N)
discharge to soil through more frequent deposition of urine and feces patches on paddocks (de Klein et
al., 2008). These factors, in turn, change microbial community growth and activity (Bardgett et al.,
1996; Bardgett et al., 2001; Bardgett and Wardle, 2003) and determine the intensity of processes
associated to nitrous oxide (N2O) flux derived from soils (de Klein et al., 2008; Levine et al., 2011;
Luo et al., 2017).
Nitrous oxide is the main GHG from soil and the second most representative between all
GHG, ranging from 15% (housed) to 25% (pasture-based) of total GHG emissions in dairy farming
systems (Aguirre-Villegas et al., 2017). Nitrous oxide is formed through microbial transformation of N
compounds in the soil, typically by incomplete denitrification or by nitrification (Wrage et al., 2001;
Saggar et al., 2013). Nitrification is an aerobic process where soil microbials oxidise NH4+ to NO3
−
and N2O is formed through chemical decomposition of intermediates, while denitrification is an
anaerobic process where NO3− is reduced into N2, with N2O an obligatory intermediate (Wrage et al.,
20
2001; de Klein and Eckard, 2008). Nitrous oxide fluxes are affected by a wide range of proximal and
distal regulators, making its regulation a very complex process (de Klein et al., 2008; Luo et al., 2017).
Proximal soil factors include mineral nitrogen (NH4+ and NO3
−) and organic carbon availabilities,
moisture, pH, temperature, and texture that are, in turn, affected by distal regulators such as rainfall or
irrigation, soil compaction, organic matter and N inputs (de Klein et al., 2008; Luo et al., 2017). In
grazed pastoral soils, the key drivers related to N2O fluxes are N inputs (i.e. excreta and fertilizer) and
soil aeration (i.e. water-filled pore space, WFPS) (de Klein et al., 2008; Luo et al., 2017). Periods
when soil characteristics favorable to N2O production coincide are called “hot moments” (Luo et al.,
2017). In tropical conditions, these “hot moments” usually occur during late spring and summer when
pastures are intensively growing owing to the abundance of solar radiation, rainfall, and N inputs.
The majority of studies involving N2O flux from pasture soils have assessed the effects of
proximal factors on processes and emission factors in temperate conditions (Saggar et al., 2013; de
Klein et al., 2014; Barneze et al., 2015; Venterea et al., 2015; Gardiner et al., 2016; Samad et al.,
2016; Clough et al., 2017; Gardiner et al., 2017; van der Weerden et al. 2017; Luo et al., 2018; Rex et
al., 2018). The little information available for tropical pastures has also focused on nitrous oxide
fluxes related to proximal factors within urine patches (Barneze et al., 2014; Lessa et al., 2014;
Mazzetto et al., 2014; Mazzetto et al., 2015). There is no information available regarding N2O fluxes
from soils of tropical pasture-based dairy farming systems, as influenced by grazing management
strategies. In fact, farming scale studies are scarce even in temperate conditions. Previous results have
shown that intensively managed grasslands are stronger sources of N2O than extensively managed
grasslands owing to greater inputs of N fertilizer and excreta (Smith et al., 2001; Flechard et al., 2007;
Rafique et al., 2011). However, they have not accounted for animal outputs that are usually greater in
intensively managed systems and could compensate the higher N2O fluxes.
2.4. Diurnal variation in herbage chemical composition and its implications to pasture-
based animal production systems
Several studies have reported diurnal variations in herbage chemical composition
(Lechtenberg et al., 1971; Orr et al., 1997; Ciavarella et al., 2000; Griggs et al., 2005; Gregorini et al.,
2006; Shewmaker et al., 2006; Gregorini et al., 2008; Morin et al., 2011; De Oliveira et al., 2018).
Such variation is mainly related to the balance between the photosynthesis and respiration processes
coupled with water loss through plant transpiration (Gregorini, 2012). Photosynthetic activity occurs
in chloroplasts mainly in the leaves, and when synthesis of carbohydrates exceeds their use the surplus
may be temporarily stored in organs present in leaves and stems (Perry and Moser, 1974; Parsons et
al., 1983). Sucrose and fructans are the predominant carbohydrate constituents of temperate grasses
(i.e. C3 metabolism), while sucrose and starch are typical in tropical grasses (i.e. C4 metabolism;
21
White, 1973; Chatterton et al., 1989; Pollock and Cairns, 1991). The surplus carbohydrate stored
inside the chloroplasts during the day is known as transitory and is used as a source of carbon to plant
respiration at night (Lu et al., 2005; Zeeman et al., 2007; Weise et al., 2011).
The balance between these processes leads to non-fibrous carbohydrate (NFC) and dry
matter (DM) concentration increases from dawn to dusk, reaching greatest concentrations between 12-
13h after sunrise (Lechtenberg et al., 1971; Morin et al., 2011; Morin et al., 2012; De Oliveira et al.,
2018). The increase of NFC occurs mainly in the upper layers of sward owing to greater proportion of
leaves rather than other plant-part components (Delagarde et al., 2000; De Oliveira et al., 2018). For
temperate swards, including grass and legumes, Pelletier et al. (2010) reported increases of soluble
carbohydrates (SC) from 6 to 105% for PM herbage compared to AM herbage; however, most results
reported mean increases around 50% (Ciavarella et al., 2000; Mayland et al., 2000; Pelletier et al.,
2010; Vasta et al., 2012; Pulido et al., 2015; Vibart et al., 2017). Increases in starch have been reported
around 100% for PM temperate legumes (Orr et al., 1997; Brito et al., 2008; Pelletier et al., 2010;
Andueza et al., 2012) and 30% for PM temperate grass swards (Orr et al., 1997; Bertrand et al., 2008;
Pelletier et al., 2010; Brito et al., 2016). Regarding DM concentration, most literature reported
increases from 14 up to 27% (Ciavarella et al., 2000; Delagarde et al., 2000; Trevaskis et al., 2001;
Gregorini et al., 2008; Abrahamse et al., 2009; De Oliveira et al., 2014; Pulido et al., 2015; Vibart et
al., 2017). Gregorini et al. (2009) explained that the diurnal changes in temperature, solar radiation,
and relative humidity, coupled with accumulation of photosynthates may explain the increase in DM
concentration from AM to PM.
The increase in NFC and DM concentrations during the day often dilutes other nutritional
entities (Gregorini et al., 2009; Gregorini, 2012; Vibart et al., 2017). Studies have reported decreases
in fiber concentration (Orr et al., 2001; Burns et al., 2007; Abrahamse et al., 2009) associated to
greater digestibility (Burns et al., 2007; Pelletier et al., 2010) for PM temperate swards. Considering
protein fractions, studies have reported decrease for PM compared to AM herbage (De Oliveira et al.,
2014; Pulido et al., 2015; Vibart et al., 2017) while other showed no effect (Gregorini et al., 2008;
Delagarde et al., 2000; Fisher et al., 2002). In fact, greater concentrations of SC and starch in the
afternoon improve the NFC/protein ratio which optimize the supply of energy and protein to rumen
microorganisms (Bryant et al., 2012; Bryant et al., 2014) reducing urinary-N discharges onto pastures
(Gregorini et al., 2010; Gregorini, 2012; Vibart et al., 2017). Moreover, Gregorini et al. (2009)
observed that diurnal increases of herbage DM and NFC concentrations, associated with dilution of
fiber concentration diminished leaf toughness and reduced particle size from AM to PM. The
fluctuations in chemical composition, toughness and particle size mean that herbage feeding value (i.e.
herbage quality) is highest during the afternoon and early evening (Gregorini, 2012).
Daily herbage intake of grazing ruminants is described by the cumulative outcome of all
meals (i.e. grazing events) during the day (Gibb, 2007). Studies have reported three to five grazing
events during cooler parts of the day (Gregorini, 2012). However, regardless of the frequency, the
22
major grazing events occur early in the morning and late in the afternoon/early evening for sheep (Orr
et al., 1997), beef cattle (Gregorini et al. 2007), and dairy cows (Gibb et al. 1998). The temporal
pattern of herbage intake, ingestive and digestive behavior of grazing ruminants can be altered by
timing of animal allocation to new strips or paddocks when subjected to intermittent stocking
management (Gibb et al., 1998; Orr et al., 2001; Gregorini et al., 2006; Gregorini et al., 2008;
Abrahamse et al., 2009; Gregorini, 2012; Pulido et al., 2015; Vibart et al., 2017). When a new
paddock or strip is allocated during the afternoon, ruminants display fewer, longer and more intensive
grazing bouts in late afternoon and early evening compared with daily morning allocation (Orr et al.,
2001; Gregorini et al., 2006; Gregorini et al., 2008; Abrahamse et al., 2009). Although these studies
have not reported that the observed shifts can increase daily herbage intake, Gregorini (2012)
suggested that ruminants moved to new fresh paddocks or strips in the afternoon may have increased
nutrient intake owing to longer grazing periods when herbage quality is at its peak, resulting in
average increases of 5% in daily milk yield (Orr et al., 2001; Abrahamse et al., 2009; Mattiauda et al.,
2013; Pulido et al., 2015; Vibart et al., 2017).
According to Janssen (2010), the nature and amount of feed are key determinants of enteric
CH4 emissions from ruminants. Therefore, diurnal variations in herbage chemical composition
associated with afternoon paddock allocation could be a strategy to mitigate enteric CH4 emissions of
livestock pasture-based systems. Modeling results have shown reductions in enteric CH4 emissions
intensity (g/kg of milk) by dairy cows when herbage NFC increases at the expense of fiber content
(Ellis et al., 2012), and Gregorini (2012) suggested the need of field research to assess this hypothesis.
2.5. Conceptual model, objectives and hypotheses
Based on literature review, a conceptual model was created aiming at integrating the
relationships among plant, animal and soil components as a function of grazing strategies in tropical
pasture-based dairy systems (Figure 1). The objective was to assess and understand the effect of two
strategies of rotational grazing management (light blue boxes; Figure 1) on plant and animal
responses, and soil parameters in an intensive tropical pasture-based dairy system.
For the first phase of the study, the central hypothesis was that changing sward structure
through strategies of rotational grazing management would optimize processes related to plant growth
(green boxes), plant-animal interface and animal responses (yellow boxes) which would in turn, affect
soil parameters that determine processes associated to N2O flux from soils (orange boxes). The
objective was to describe and measure the influence of two pre-grazing targets (LI95% and LIMax) on
herbage accumulation of elephant grass (Pennisetum purpureum Schum. cv. Cameroon), milk outputs
of Holstein × Jersey cows, and soil parameters (N2O emission and WFPS, soil NH4+ and soil NO3
−).
23
Figure 1. Conceptual model - Blue boxes: controlled factors (treatments); green
boxes: plant responses; yellow boxes: animal responses; orange boxes: soil parameters
Once the ideal pre-grazing target (LI95% or LIMax) was established during the first phase, the
second step consisted of a refinement of the first phase. The hypothesis was that the diurnal variation
in herbage chemical composition of elephant grass coupled with afternoon allocation of the herd to a
new fresh paddock would increase nutrient intake and milk outputs, and decrease the intensity of
enteric CH4 emission of Holstein × Jersey cows. The objective was to describe and measure the
influence of two timings of paddock allocation (AM and PM) on elephant grass herbage chemical
composition and milk outputs of Holstein × Jersey cows.
References
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grass in morning or afternoon on intake, grazing behaviour, rumen fermentation and milk
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3. STRATEGIC GRAZING MANAGEMENT TOWARDS SUSTAINABLE
INTENSIFICATION AT TROPICAL PASTURE-BASED DAIRY SYSTEMS1
Abstract
Agricultural systems are responsible for environmental impacts that can be mitigated through
the adoption of more sustainable practices. The objective of this study was to investigate the influence
of two pre-grazing targets (95% and maximum canopy light interception during pasture regrowth;
LI95% and LIMax, respectively) on sward structure and herbage nutritive value of rotationally grazed
elephant grass (Pennisetum purpureum Schum. cv. Cameroon), and dry matter intake (DMI), milk
yield, stocking rate, enteric methane (CH4) emissions by Holstein × Jersey dairy cows. It was
hypothesized that grazing strategies can modify sward structure and improve the nutritive value of the
consumed herbage, increasing DMI and reducing the intensity of enteric CH4 emissions, providing
environmental and productivity benefits to tropical pasture-based dairy systems. Results indicated that
pre-grazing sward surface height was greater for LIMax (≈135 cm) than LI95% (≈100 cm) and can be
used as a reliable field guide for monitoring sward structure. Grazing management based on LI95%
criterion improved herbage nutritive value and grazing efficiency, allowing greater DMI, milk yield
and stocking rate by dairy cows. Daily enteric CH4 emission was not affected; however, cows grazing
elephant grass at LI95% were more efficient and emitted 21% less CH4/kg of milk yield and 18% less
CH4/kg of DMI. The 51% increase in milk yield per hectare overcame the 29% increase in enteric CH4
emissions per hectare in LI95% grazing management. Thereby the same resource allocation resulted in a
16% mitigation of the main greenhouse gas from pasture-based dairy systems. Overall, strategic
grazing management is an environmentally friendly practice that improves the use efficiency of
allocated resources through optimization of processes involving plant, ruminant and their interface,
and enhances milk production efficiency of tropical pasture-based systems.
Keywords: Canopy light interception; Enteric methane emissions; Herbage quality; Land-use
improvement; Milk production efficiency; Elephant grass
3.1. Introduction
To meet the world's future food demand and environmental needs, agricultural outputs must
grow from 60 to 120% (Godfray et al., 2010; Conforti, 2011; Alexandratos and Bruinsma, 2012) while
agriculture environmental footprint must decrease dramatically (Foley et al., 2011). In developing
countries, agriculture production must increase 80% through higher yields resulting from
intensification of existing agricultural systems (Conforti, 2011). Sustainable intensification was
defined as a form of production wherein yields are increased without adverse environmental impact
and without the cultivation of more land (Royal Society, 2009). Despite contested (Struik and Kuyper,
2017), this term was deeply discussed (Pretty and Bharucha, 2014) and highlights the needs to
increase the productivity (i.e. agricultural product outputs per hectare) of current agricultural systems
through practices that minimize key environmental issues (Garnett and Godfray, 2012).
1Congio, G.F.S., Batalha, C.D.A., Chiavegato, M.B., Berndt, A., Oliveira, P.P.A., Frighetto, R.T.S., Maxwell, T.M.R., Gregorini, P., Da
Silva, S.C., 2018. Strategic grazing management towards sustainable intensification at tropical pasture-based dairy systems. Sci. Total Environ. 636:872–880. DOI: 10.1016/j.scitotenv.2018.04.301
38
Intensification of pasture-based dairy systems has been associated with increasing inputs
such as nitrogen fertilizer or imported supplements (Beukes et al., 2012; Foote et al., 2015; Macdonald
et al., 2017). However, such intensification practices are associated with issues of environmental
concern, namely increased greenhouse gases (GHG) emissions, water and land degradation (Foley et
al., 2011; Vogeler et al., 2013; Foote et al., 2015). Alternatively, grazing management strategies that
optimize herbage utilization and digestible dry matter intake (DMI) by grazing cows could improve
land-use and mitigate key environmental issues of pasture-based dairy systems (Muñoz et al., 2016;
Gregorini et al., 2017).
Plant growth is a function of canopy light interception (LI) and leaf area index (LAI), with
the accumulation of herbage fitted to a sigmoid curve with three distinct phases (Brougham, 1955).
During the early stages of regrowth, leaves are the morphological component accumulated the most.
As LAI increases, canopy light intra-competition increases and plants change their growth pattern as a
means of optimizing light capture through stem elongation. The shift in growth pattern occurs when
canopy LI reaches and exceeds 95% (LI95%; Da Silva et al., 2015). Intermittent grazing practices (i.e.
rotational stocking), interrupting regrowth at LI95%, leads to a greater leaf accumulation (Pereira et al.,
2014; Pereira et al., 2015b), higher tiller population density and soil cover (Pereira et al., 2015a) than
grazing at maximum light interception (LIMax). In addition, sward grazed at LI95% has been reported to
have herbage of greater nutritive value (Trindade et al., 2007) and less herbage losses (Silveira et al.,
2013).
Considering the grazing animal, pre-grazing management targets which optimize leaf
production and nutritive value (LI95%) would maximize herbage DMI owing to the greater proportion
of leaves in the grazing strata (Da Silva and Carvalho, 2005; Gregorini et al., 2011). Optimum short-
term intake rate by dairy heifers grazing guinea grass was obtained when sward intercepted 95% of the
incident light (Carnevalli et al., 2006; Palhano et al., 2007). Enteric methane (CH4) is the predominant
source of GHG emissions in livestock systems (Crosson et al., 2011; Guerci et al., 2013), ranging from
30% (high feed concentrate levels) to 83.5% (pasture-based) of total GHG emissions in dairy farming
systems (Aguirre-Villegas et al., 2017). Enteric CH4 production from animal digestion is associated
with feed intake and herbage chemical composition (Janssen, 2010). In temperate grasslands, grazing
strategies can be used to reduce the CH4 emission intensity (i.e. CH4/kg of product) and CH4 yield (i.e.
CH4/kg of DMI) (Wims et al., 2010; Boland et al., 2013; Muñoz et al., 2016).
Although the studies aforementioned have demonstrated the benefits of grazing strategies
based on LI95% criteria, most focused solely on plant responses. There is a knowledge gap in
relationships among plant and animal responses and environmental benefits in tropical pasture-based
dairy systems. The central hypothesis of this study is that the change in sward structure caused by
LI95% management would optimize processes related to plant growth, plant-animal interface and
between animal-rumen microorganisms delivering improved environmental services to the system by
reducing CH4 emission intensity and increased milk productivity. Our objective was to investigate the
39
influence of strategic grazing with pre-grazing targets (LI95% and LIMax) on enteric CH4 emissions and
animal productivity in dairy tropical pasture of elephant grass (Pennisetum purpureum Schum. cv.
Cameroon).
3.2. Material and Methods
All procedures for this study were approved by the Animal (15.5.1246.11.2) and
Environment Ethics Committees (17.5.999.11.9) at the University of São Paulo, College of
Agriculture “Luiz de Queiroz” (USP/ESALQ).
3.2.1. Study site
The experiment was conducted in Piracicaba, SP, Brazil (22°42′S, 47°38′W and 546 a.s.l.)
on a rainfed, non-irrigated elephant grass (Pennisetum purpureum Schum. cv. Cameroon) pasture
established in 1972 in a high fertility Eutroferric Red Nitossol (Pereira et al., 2014). The climate is
sub-tropical with dry winters and 1328 mm average annual rainfall (CEPAGRI, 2012). The lowest and
highest mean temperatures were recorded in July (19.7 °C) and December (27.1 °C), respectively. The
greatest accumulated rainfall was observed from late spring to summer (1090 mm from November
2015 to March 2016), and the lowest from winter to early spring (356 mm from June to October
2015).
3.2.2. Treatments and experimental design
The two treatments were pre-grazing targets of either 95% or maximum canopy light
interception during regrowth (LI95% and LIMax, respectively). Treatments were allocated to
experimental units (2058 m2 paddocks) according to a randomized complete block design, with six
replications. The slope and chemical soil characteristics were considered as blocking criteria.
Before treatment implementation, paddocks were grazed and mowed to 45 cm for
standardization in mid-January 2015. The pre-grazing targets of LI95% and LIMax were maintained until
late November 2015 (adaptation period). This period was necessary to adapt sward structure to
treatments and to identify the pre-grazing sward surface height (SSH) for the pre-grazing targets (LI95%
and LIMax). For both treatments, the herbage depletion level (HDL) corresponded to 50% of the pre-
grazing SSH as a means to maintain high short-term rates of herbage intake (Fonseca et al., 2012;
Carvalho, 2013). The pre- and post-grazing SSH were measured from ground level to the top leafy
horizon by 40 systematic readings per paddock, using a stick graduated in centimeters. Canopy LI was
40
monitored using a LAI 2000 canopy analyzer (LI-COR, Lincoln, NE, USA) to take six readings above
the canopy and thirty at ground level per experimental unit (Pereira et al., 2014).
Measurements were performed after the adaptation period throughout the experimental
period (from December 4th 2015 to April 3th 2016 – 119 days), which was divided into three
sampling periods of forty days (early summer, full summer and late summer). During the experimental
period, pre-grazing targets for grazing management treatments were based on the heights
corresponding to the LI treatments determined during the adaptation period. A total of 215 kg N/ha (as
urea, 45% of N) was applied throughout the experimental period. Because the grazing interval was not
constant (as a consequence of experimental treatments design), the total amount of N to be applied
was divided throughout the experimental period (119 days) and a daily rate of nitrogen fertilization
was calculated. The amount of N applied per paddock after each grazing was proportional to the
length of the corresponding rest period (daily rate × rest period), ensuring similar N fertilization to
both treatments at the end of the experimental period (Da Silva et al., 2017).
3.2.3. Plant measurements
Frequency of tussocks, bare ground, and weeds as well as tussock perimeter were measured
five times throughout the adaptation and experimental periods. At the beginning of a regrowth cycle, a
nylon string transect was placed within the paddock, with readings taken to identify botanical
composition every two meters. Tussocks present at each point had their perimeter measured at ground
level using a metric tape. A total of 100-points were sampled per paddock and the frequency of each
botanical component was calculated as a proportion of the total number of reading points (Pereira et
al., 2015a). At the last evaluation of botanical composition, tiller population density was determined
by counting the total number of tillers in three rectangular sub-samples (0.94 m2 each) randomized per
paddock.
At the beginning of the experimental period, each paddock was divided up into three sub-
paddocks (686 m2) with plant measurements performed within the central sub-paddock. The SSH was
measured as described above with 40 readings per sub-paddock. Pre-grazing herbage mass was
quantified in each grazing cycle from three rectangular samples collected randomly (0.94 m2 each)
from each sub-paddock. The herbage was clipped above post-grazing SSH according to each
treatment, weighed fresh, and two sub-samples taken to the laboratory. One sub-sample was used to
determine plant-part components by hand separation into leaf (leaf blades), stem (stems + leaf sheaths)
and dead material. The second sub-sample was used to determine herbage chemical composition. Both
samples were dried in a forced-air drier at 65 °C to constant weight. Herbage and morphological
components accumulation represent the sum of pre-grazing herbage mass throughout the experimental
period. Samples to determine herbage chemical composition were ground through a 1-mm screen
41
(Wiley Mill, Thomas Scientific, Philadelphia, PA). Dry matter (DM) and ash concentrations were
determined at 105 °C for 24 h and 600 °C for 4 h, respectively (AOAC International, 2005). Neutral
detergent fiber (NDF), acid detergent fiber (ADF) and lignin concentrations were determined
sequentially (Van Soest et al., 1991). Total nitrogen (N) concentration was determined by the Dumas
combustion method using N analyzer (Leco FP-2000 N Analyzer; Leco Instruments Inc., St. Joseph,
MI, USA), and crude protein (CP) concentration calculated as N × 6.25.
Grazing losses were estimated from two randomized samples (0.94 m2 each) per paddock. At
pre-grazing, rectangular frames were placed on the soil surface and all litter removed leaving a clean
soil surface. After grazing, these areas were revisited and all material lying on the ground as well as
broken stems and green leaves still attached and hanging on tussocks were collected, weighed fresh,
and dried in a forced-air drier at 65 °C to constant weight (Silveira et al., 2013). Grazing losses were
expressed in DM/ha and as a percentage of the pre-grazing herbage mass (above post-grazing SSH)
and its complement to 100 was considered as grazing efficiency (Carnevalli et al., 2006). Herbage and
leaf allowance were calculated by the relationship between pre-grazing herbage mass (above post-
grazing SSH) and number of cows per day (Pérez-Prieto and Delagarde, 2013).
3.2.4. Herd and feeding
Twenty-six Holstein × Jersey dairy cows averaging 488 ± 60 kg body weight (BW) (mean ±
SD), 2.94 ± 0.18 body condition score (BCS), daily milk yield of 20.3 ± 2.6 kg/d, and 126 ± 90 days in
milk (DIM) were stratified and grouped in pairs into 13 blocks according to daily milk yield and DIM,
and then randomly assigned to either LI95% and LIMax grazing management. An additional herd of dry-
cows (10 to 13 cows) was maintained in an adjacent area of elephant grass and was used to keep
grazing management targets constant, as needed. The stocking rate was calculated by number of cows
used daily for each treatment, considering experimental cows and the additional herd.
Concentrate meals were fed individually twice daily (4:30 am and 2:30 pm) before milking
(5 am and 3 pm) at a rate of 1 kg of concentrate/3 kg of milk (considering the average of each block).
The rate was established based on milk yield at the beginning of each sampling period (Danes et al.,
2013). The concentrate meal was composed of citrus pulp (35%), corn gluten feed (30%), fine ground
corn (20%), soybean meal (10%) and mineral (5%), with chemical composition as followed: 88.4%
DM, 10.3% ash, 14.0% CP, 22.2% NDF, 9.3% ADF, 3.3% ether extract and 49.8% non-fibrous
carbohydrate.
42
3.2.5. Animal measurements
The BW and BCS were measured at the end of each sampling period over three consecutive
days (Edmonson et al., 1989). Milk yield was recorded daily with samples collected in vials containing
bronopol preservative pill and analyzed for fat, protein, lactose, and milk solids using infrared
procedures (MilkoScan FT+; Foss North America Inc., Eden Prairie, MN).
Herbage intake was estimated from total fecal excretion and feed indigestibility. To estimate
total fecal excretion, titanium dioxide (TiO2) was dosed twice a day (20 g/cow per day) after
concentrate meals over 12 days. Fecal samples were collected from the rectum after concentrate meals
on the last 5 days, dried in a forced-air drier at 55 °C for 72 h, ground through a 1-mm screen
(WileyMill, Thomas Scientific, Philadelphia, PA) and composited forming one sample per sampling
period by cow. Titanium dioxide concentration in feces was determined according to Myers et al.
(2004). To determine the feed indigestibility, the indigestible NDF (iNDF) content of herbage,
concentrate, and fecal samples were estimated by 240 h in vitro incubation (Goeser and Combs, 2009).
Total fecal excretion, fecal excretion from concentrate, and herbage intake were calculated according
to De Souza et al. (2015).
Enteric CH4 emissions were estimated using sulfur hexafluoride (SF6) as tracer gas (Johnson
and Johnson, 1995). Pre-calibrated permeation tubes containing SF6 with known release rates (1.41 ±
0.40 mg/day) were placed into the rumen of each cow 72 h prior to the first collection. Sampling
apparatus included a PVC collection canister (2.3 L) and adjustable halter containing stainless steel
capillary tubing and brass connections. The cows were adapted to the sampling apparatus over 7 days
prior to collection with CH4 emissions measured at 24-hour intervals over 7 consecutive days.
Canisters were vacuumed to approximately −13.5 psi using a three-stage vacuum pump (Symbol,
Sumaré, SP, Brazil) and Druck DPI 705 digital manometer (GE Druck, South Burlington, VT, EUA)
and replaced daily just after the afternoon concentrate meal. Background SF6 and CH4 concentrations
were determined using two sampling apparatus placed daily in the field near the grazing herd.
Methane and SF6 concentrations were determined at the Laboratory of Biogeochemistry and Tracer
Gases Analysis (Embrapa Meio Ambiente, Jaguariúna, SP, BRA) using gas chromatography (HP6890,
Agilent, Delaware, USA). Prior to chromatograph determination, canisters were pressurized to 1.3–1.5
psi with ultrapure nitrogen 5.0, and pressures recorded by Druck DPI 705 digital manometer (GE
Druck, South Burlington, VT, EUA) in order to calculate the dilution factor. The chromatograph was
equipped with a flame ionization detector (FID) at 280 °C for CH4 (column megabore, 0.53 mm × 30
m × 15 μm, Plot HP-Al/M) and an electron capture detector (ECD) at 300 °C for SF6 (column
megabore, 0.53 mm × 30 m × 25 μm, HP-MolSiv), with two loops of 0.5 cm3 maintained at 80 °C
attached to 2 six-way valves. Calibration curves were established using standard certified gases for
CH4 (4.85 ± 5%; 9.96 ± 1.65% and 19.1 ± 3.44% ppm) and SF6 (34.0 ± 9.0; 91.0 ± 9.0 and 978.0 ±
98.0 ppt) (Westberg et al., 1998). Daily methane emission was calculated from collected SF6 and CH4
43
concentrations in the canisters discounting background concentrations, and the value of SF6
permeation tube release rate (Johnson and Johnson, 1995).
3.2.6. Statistical analysis
Analysis of variance was performed using the Mixed Procedure (SAS 9.3; SAS Institute Inc.,
Cary, NC). Different structures of the variance˗covariance matrices were tested and the Bayesian
Information Criterion was adopted to select the best fit matrix. Within plant parameters, the paddock
was considered the experimental unit, and for animal measurements, the cow was considered the
experimental unit. Blocks were considered random terms, and LI, sampling periods and their
interactions were treated as fixed effects. Sampling periods were treated as repeated measures. For
tussock measurements, season of the year was considered a fixed effect because assessments were
made throughout the entire study period (adaptation + experimental). Means were calculated using the
LSMEANS statement, compared using the Student's t-test and the Bonferroni adjustment. Differences
were declared significant at P ≤ 0.05, and trends were declared at P ≤ 0.10.
3.3. Results
3.3.1. Canopy light interception and sward surface height
Grazing management targets and sward characteristics during the adaptation and
experimental periods are presented in Table 1. The LI95% pre-grazing target was reached at 99 cm
(≈100 cm) and the LIMax pre-grazing target was reached at 134.5 cm (≈135 cm). For both treatments,
HDL was close to the target of 50% of the pre-grazing SSH, and corresponded to post-grazing heights
of 50.4 and 64.3 cm for LI95% and LIMax, respectively.
44
Table 1. Canopy light interception, pre- and post- grazing
sward surface height (SSH) and herbage depletion level
(HDL) of elephant grass subjected to strategies of rotational
stocking management (LI95% or LIMax) during the adaptation
(Jan-Nov 2015) and experimental (Dec 2015-Apr 2016)
periods (n = 6)
Period Treatments SEM1 P-value
Adaptation LI95% LIMax
Light interception, % 95.2 98.0 0.11 <0.0001 Pre-SSH, cm 99.0 134.5 0.94 <0.0001
Post-SSH, cm 50.4 64.3 0.86 <0.0001
HDL, % of Pre-SSH 49.0 50.8 0.57 0.03
Experimental
Pre-SSH, cm 99.7 134.4 0.58 <0.0001 Post-SSH, cm 50.9 68.3 0.45 <0.0001
HDL, % of Pre-SSH 49.1 48.9 0.46 0.7269 1Standard error of the mean
3.3.2. Canopy cover
Frequencies of tussocks and bare ground, and tussock perimeter were affected by season (P
< 0.01) indicating a strong effect of growth conditions on plant ecophysiology (Table 2). Across
seasons of the year, tussocks showed a tendency of greater frequency (P = 0.06) for LI95% than LIMax
(38% and 33%, respectively). Inversely, the frequency of bare ground was greater (P = 0.04) for LIMax
than LI95% (54% and 50%, respectively). There was no effect of LI pre-grazing on tussock perimeter.
However, in the second summer, tussocks under LI95% management had a greater perimeter than LIMax
(P < 0.05). Tiller population density was greater (P < 0.01) for LI95% relative to LIMax.
Table 2. Frequencies of tussock and bare ground, tussock perimeter and tiller population density of
elephant grass subjected to strategies of rotational stocking management (LI95% or LIMax) during the
adaptation (Jan-Nov 2015) and experimental (Dec 2015-Apr 2016) periods (n = 6)
Seasons1 SEM2 P-value
S1 A/W ES LS S2 Trt3 Per4 Trt×Per
Frequency of tussocks, %
LI95% 32.8 Ab 33.7 Ab 28.6 Ab 35.7 Ab 52.9 Aa 3.30 0.0633 <0.0001 0.6955 LIMax 32.9 Ab 29.9 Ab 27.5 Ab 26.8 Ab 47.7 Aa
Frequency of bare ground, %
LI95% 54.0 Aa 54.9 Aa 59.7 Aa 51.8 Aa 28.8 Ab 3.23 0.041 <0.0001 0.8760 LIMax 56.2 Aa 57.4 Aa 62.2 Aa 60.5 Aa 32.4 Ab
Tussock perimeter, cm
LI95% 174 Ab 186 Ab 193 Ab 165 Ab 236 Aa 9.15 0.795 <0.0001 0.0604 LIMax 185 Aa 186 Aa 183 Aa 187 Aa 205 Ba
Tiller population density, tiller/m2
LI95% - - - - 129.1 6.53 0.0049 - - LIMax - - - - 87.3
Means followed by the same capital letter in columns and the lower case letter in rows do not differ (P > 0.05)
1S1: Summer 1 – Mar 2015, A/W: Autumn/Winter – Jun/Jul 2015, ES: Early Spring – Sep 2015, LS: Late Spring – Nov 2015 and
S2: Summer 2 – Dec 2015 2Standard error of the mean 3Treatment effect 4Sampling period effect
45
3.3.3. Herbage characteristics
The LI95% provided more grazing cycles (P < 0.01) associated with lower stocking and
shorter rest periods than LIMax (P < 0.01) (Table 3). Longer rest periods of LIMax allowed greater plant
growth and determined higher pre-grazing herbage mass (P < 0.01) with more stems (P < 0.01), lower
leaf blade (P < 0.01) and lower leaf:stem ratio (P < 0.01) than LI95%. There was no treatment effect on
dead material (P = 0.31). Herbage accumulation was not affected by LI strategy (P = 0.11) but more
frequent defoliation (LI95%) resulted in greater leaf (P = 0.01) and lower stem (P < 0.01) accumulation
throughout the experimental period (Table 3). Daily herbage allowances were greater for LIMax than
LI95% (P = 0.02). The LI95% promoted lower grazing losses (P < 0.01) and more efficient grazing (P <
0.01) than LIMax. Grazing management strategy influenced herbage chemical composition, with LI95%
herbage having greater CP (P < 0.01), and lower ADF (P = 0.03) and lower lignin (P < 0.01; Table 3).
There was no treatment effect on DM (P = 0.70), NDF (P = 0.11), and ash (P = 0.28) (Table 3).
Table 3. Grazing cycles, stocking period, rest period, pre-grazing herbage characteristics, herbage
accumulation, herbage allowance, grazing losses, grazing efficiency and herbage chemical composition
of elephant grass subjected to strategies of rotational stocking management (LI95% or LIMax) during the
experimental period (Dec 2015-Apr 2016) (n = 6)
Item Treatments SEM1 P-value
LI95% LIMax Trt2 Per3 Trt×Per
Grazing cycles, n 5.6 3.5 0.16 <0.0001 0.1330 0.2430 Stocking period, days 1.0 1.4 0.06 <0.0001 0.0582 0.0917
Rest period, days 21.1 31.7 0.60 <0.0001 <0.0001 0.5215
Pre-grazing herbage mass4, kg of DM/ha 2890 4890 207.9 <0.0001 0.3625 0.3410
Leaf blade4, % 95.1 81.1 0.93 <0.0001 0.7648 0.1049
Stem4, % 3.5 16.6 0.52 <0.0001 0.0919 0.2000
Dead material4, % 1.4 2.3 0.20 0.3132 0.0137 0.4464
Leaf : Stem ratio4 32.8 4.6 3.50 <0.0001 0.0011 0.0004
Herbage accumulation4, kg of DM/ha 15441 16683 847.0 0.1079 - -
Leaf accumulation4, kg of DM/ha 14611 13276 730.2 0.0134 - -
Stem accumulation4, kg of DM/ha 795 3322 264.2 <0.0001 - -
Herbage allowance4, kg of DM/cow.day 21.2 29.7 2.53 0.016 0.5026 0.4409
Grazing losses, kg of DM/ha 292 1203 100.3 <0.0001 <0.0001 <0.0001
Grazing efficiency, % 89.3 79.9 2.88 0.0003 0.004 0.7755
Herbage chemical composition, % DM
Dry matter 19.5 19.2 0.91 0.6992 0.0724 0.2753 Crude protein 21.0 19.4 0.50 0.0004 <0.0001 0.0387
Neutral detergent fiber 61.2 63.0 1.26 0.1121 0.1438 0.2865
Acid detergent fiber 33.9 36.3 1.14 0.0248 0.2603 0.7348
Lignin 3.3 3.8 0.16 0.0040 0.2429 0.4676
Ash 10.4 11.2 0.54 0.2816 0.0228 0.6754 1Standard error of the mean 2Treatment effect 3Sampling period effect 4Estimated above post-grazing SSH
46
3.3.4. Dry matter intake, animal performance and CH4 emissions
Animal responses are presented in Table 4. Stocking rate was 33% greater for LI95% than
LIMax (P < 0.01). Greater herbage (P < 0.01) and total DMI (P < 0.01) were observed for LI95% than
LIMax. Grazing at LI95% resulted in 15.3% greater milk yield (P < 0.01), 8.9% more fat corrected milk
(P = 0.02), 8% greater protein (P < 0.01), 15.3% more lactose (P < 0.01) and 6.2% greater milk solids
(P < 0.01) yields. Fat yield was not affected by treatment (P = 0.20). There were no LI effects on BW
and BCS changes (P = 0.61 and P = 0.13, respectively). Daily enteric CH4 emission (g/d) was not
affected by treatment (P = 0.85), however LI95% grazing management increased the efficiency of milk
(P < 0.01), fat (P < 0.01), protein (P < 0.01) and milk solids (P < 0.01) yields per g of CH4 emitted by
21%, 15%, 13% and 16%, respectively. Additionally, cows grazing elephant grass managed with the
LI95% pre-grazing target had lower CH4 yield (P = 0.02).
Table 4. Stocking rate, daily dry matter intake (DMI), milk yield and enteric CH4
emissions of cows grazing elephant grass subjected to strategies of rotational stocking
management (LI95% or LIMax) during the experimental period (Dec 2015-Apr 2016) (n =
13)
Item Treatments SEM1 P-value
LI95% LIMax Trt2 Per3 Trt×Per
Stocking rate, cows/ha 9.3 7.0 0.98 0.0054 - -
Daily DMI, kg of DM/cow
Herbage 12.3 10.1 0.52 0.0017 0.0071 0.1781 Total 18.2 15.9 0.61 0.0028 0.1113 0.2177
Yield, kg/d
Milk 18.1 15.7 1.01 <0.0001 <0.0001 0.7768 3.5% FCM4 18.4 16.9 1.18 0.0205 0.0156 0.6329
Fat 0.646 0.608 0.0407 0.2004 0.0709 0.355
Protein 0.556 0.515 0.0274 0.0098 0.001 0.8361
Lactose 0.792 0.687 0.0461 <0.0001 <0.0001 0.5586
Milk solids 2.07 1.95 0.1084 0.0059 0.0004 0.7953
BW5 change, kg/d 0.4375 0.5530 0.36 0.6107 0.0031 0.6086
BCS6 change -0.06 0.01 0.05 0.1330 0.8276 0.7137
CH4 emissions
g/d 297.8 296.1 13.30 0.8533 <0.0001 0.8978 g/kg of milk yield 16.2 20.5 1.09 <0.0001 <0.0001 0.1365
g/kg of fat yield 438.9 515.3 24.71 0.0005 <0.0001 0.9986
g/kg of protein yield 525.2 604.6 22.40 <0.0001 <0.0001 0.1931
g/kg of milk solids yield 133.5 159.5 6.71 <0.0001 <0.0001 0.4666
g/kg of dry matter intake 20.2 24.7 1.33 0.0199 <0.0001 0.0012 1Standard error of the mean 2Treatment effect 3Sampling period effect 43.5% Fat Corrected Milk = [(0.4324 × milk yield) + (16.216 × fat yield)] 5BW: body weight 6BCS: body condition score
47
3.3.5. Milk yield and CH4 emissions per hectare
Milk yield and CH4 emissions per hectare are presented in Table 5. Grazing at LI95%
increased milk yield by 51% (P < 0.01), fat yield by 42% (P = 0.02), protein yield by 41% (P < 0.01)
and milk solids yield by 40% (P < 0.01) per hectare. The enteric CH4 emitted per hectare was 29%
greater for LI95% than LIMax (P < 0.01). Additionally, LI95% pre-grazing target increased productivity of
milk (P < 0.01), protein (P < 0.01) and milk solids (P < 0.01) per kg of CH4 released per hectare by
16%, 9% and 7%, respectively (Table 5).
Table 5. Milk yield and CH4 emissions per hectare (ha) of cows
grazing elephant grass subjected to strategies of rotational stocking
management (LI95% or LIMax) during the experimental period (Dec
2015-Apr 2016) (n = 6)
Item Treatments SEM1 P-value
LI95% LIMax
Milk, kg/ha.day 169.8 112.4 20.40 0.0012 Fat, kg/ha.day 6.1 4.3 0.71 0.0148
Protein, kg/ha.day 5.2 3.7 0.62 0.0026
Milk solids, kg/ha.day 19.5 13.9 2.42 0.0057
CH4, kg/ha per day 2.7 2.1 0.24 0.0055
Productivity vs. CH4
Milk, kg/kg of CH4/ha.day 62.2 53.8 4.79 0.0002 Fat, kg/kg of CH4/ha.day 2.22 2.06 0.132 0.1151
Protein, kg/kg of CH4/ha.day 1.91 1.76 0.135 <0.0001
Milk solids, kg/kg of CH4/ha.day 7.13 6.66 0.550 0.0098 1Standard error of the mean
3.4. Discussion
Grazing strategies that maintain shorter rather than taller pre-grazing SSH normally result in
greater canopy cover, associated with greater frequency of tussocks and tiller population density
(Pereira et al., 2015a). Greater canopy cover by plants reduces nutrient and soil transport avoiding
contamination and sedimentation of waterways (McDowell and Houlbrooke, 2009). Furthermore,
competition for light is stronger in taller swards, resulting in tiller death, reduced tillering, less stability
of plant population, greater frequency of bare ground and reduced sward perennation (Pereira et al.,
2015a). The differences observed in tussock perimeter during the second summer, one year after the
adaptation period, indicate that elephant grass adapts its horizontal structure to defoliation regimes
slowly. This feature is an important aspect to be considered for planning of field experiments and
management strategies (Pereira et al., 2015a).
The LI95% pre-grazing target results in maximum net herbage accumulation rate and is
considered the critical LAI (Brougham, 1958). For tropical forage, this condition is associated with the
beginning of marked stem elongation (Da Silva et al., 2015). Pre-grazing SSH is strongly correlated
with LAI and LI, and can be used as a reliable field indicator for controlling herbage regrowth.
48
Grazing strategies based on the maximum LI pre-grazing target result in longer regrowth periods,
increasing light competition within the sward, shifting the plant's growth pattern to prioritize stem
elongation and resulting in greater stem and lower leaf accumulation (Da Silva et al., 2009). In the
present study, the 3-percentual unit increase in LI pre-grazing for the LIMax relative to the LI95% target
resulted in 50% increase of the rest period. Total herbage accumulation was similar between
treatments. However, LI95% increased leaf accumulation by 10% and decreased stem accumulation by
76% compared to LIMax, as previously shown (Carnevalli et al., 2006; Barbosa et al., 2007; Silveira et
al., 2013; Pereira et al., 2014).
In pasture-based systems, only a portion of the herbage accumulated is consumed by grazing
animals. The remaining fraction is lost as a consequence of trampling and characterizes grazing
inefficiency (Carnevalli et al., 2006; Silveira et al., 2013). Grazing at LIMax resulted in fourfold higher
grazing losses than LI95%. This suggests that taller swards result in greater grazing losses, as observed
by previous studies in tropical conditions (Carnevalli et al., 2006; Silveira et al., 2013). Inversely, the
grazing efficiency was ten percentage units higher for LI95% than LIMax, corroborating the findings of
Carnevalli et al. (2006) and Silveira et al. (2013).
The stocking rate was 33% greater for LI95% than LIMax. Voltolini et al. (2010) and Gimenes
et al. (2011) found stocking rate increases ranging from 10 to 42% in elephant grass and palisade grass
pastures adopting the 95% pre-grazing target of canopy light interception under rotational grazing
management. Greater stocking rates are supported by greater leaf accumulation associated with lower
grazing losses determining greater grazing efficiency. In the present study, LI95% had 10% greater leaf
accumulation associated with fourfold reduction in grazing losses. The lower grazing efficiency
observed in taller swards may also be associated with higher stem accumulation and increased
senescence and dead material (Pereira et al., 2014; Pereira et al., 2015b), which are plant-part
components avoided by grazers (Trindade et al., 2007).
Chemical composition of the herbage is a function of the proportions of plant-part
components and their tissue anatomy (Moore, 1994). In the present study, stem proportions were 3.5%
for LI95% and 16.6% for LIMax and, leaf blade was 95.1% for LI95% and 81.1% for LIMax. The ADF is
composed of cellulose and lignin, present mainly in the cell wall, associated with structural support for
plant organs (Moore and Jung, 2001). Stems contain a higher proportion of cell wall tissues and less
photosynthetic tissues than leaves (Wilson and Kennedy, 1996). On the other hand, most protein
compounds are present in leaves, with the majority associated with photosynthetic enzymes (Gastal
and Durand, 2000).
Daily herbage intake is determined by interactions between sward structure and grazing
animals (Wade and Carvalho, 2000). Poppi et al. (1987) suggested that herbage intake by grazing
animals follows an asymptotic distribution represented by two distinct phases. In the first ascending
phase, herbage intake is related to sward structure (herbage or leaf mass, pre-SSH, leaf-to-stem ratio)
and grazing behavior (grazing time, diet selection, bite mass and bite rate), which are characteristics
49
strongly affected by grazing management strategies (Da Silva and Carvalho, 2005). In the second
asymptotic phase, nutritional factors such as herbage chemical composition, time of herbage retention
in the rumen and concentration of metabolic compounds are more relevant in controlling intake (Poppi
et al., 1987). In the present study, it is likely that sward structure characteristics, such as pre-grazing
SSH and plant-part components strongly affected grazing behavior and ultimately herbage DMI.
Swards constantly kept at taller heights (such as LIMax swards) result in lower short-term intake rate at
the beginning of grazing, due to the excessive length of leaf blade associated with lower bulk density
of herbage in the upper strata which increases time per bite (Palhano et al., 2007; Fonseca et al., 2013;
Carvalho, 2013). At the end of grazing, greater proportion of stems results in physical constraints
reducing herbage intake (Laca and Lemaire, 2000; Fonseca et al., 2012; Carvalho, 2013). At the rumen
level, more fibrous herbage (i.e. higher NDF, ADF and lignin) is associated with greater ruminal
retention time, lower fermentation and passage rate, and lower herbage intake (Mertens, 1994; Allen,
1996; Allen, 2000; Forbes, 2007).
Herbage DMI is a key determinant of performance of livestock-based systems (Mertens,
1994; Poppi et al., 1997; Sollenberger and Burns, 2001; Coleman and Moore, 2003; Sollenberger and
Vanzant, 2011). Coleman and Moore (2003) described that the combination of herbage chemical
composition (i.e. nutritive value), nutrient availability (i.e. digestibility), and intake determines
herbage quality (i.e. feed value) and is accepted as an indicator of animal performance. Our results
indicate that it is possible to produce herbage with high nutritive value resulting in greater herbage
DMI, and milk yield through pre-grazing SSH that avoids excessive stem elongation (LI95%). These
results corroborate those of experiments conducted in temperate climates that determined greater
nutritive value, DMI and milk yield in low herbage mass swards compared to high herbage mass
swards (Wims et al., 2010; Muñoz et al., 2016). In the present study, greater milk yield was observed
at higher stocking rates and lower herbage allowance (LI95%) demonstrating that in tropical swards, the
distribution and arrangement of above-ground plant parts (i.e. sward structure, see Laca and Lemaire,
2000) plays a more important role than herbage allowance in determining herbage DMI and animal
performance.
Enteric CH4 is affected by the amount and nature of feed, and the extent of its degradation,
which in turn determines the amount of hydrogen formed in the rumen (Janssen, 2010). Although DMI
and herbage nutritive value have been affected by targets of pre-grazing LI, daily enteric CH4 emission
was not. Similarly, studies with temperate grasses did not observe differences in daily enteric CH4
emission from beef heifers (Boland et al., 2013) and dairy cows (Wims et al., 2010; Muñoz et al.,
2016) grazing low herbage mass and high herbage mass swards, even with significant differences
reported in DMI and nutritive value. The model proposed by Janssen (2010) suggests that greater
passage rates increase hydrogen concentration in the rumen. Consequently, microorganisms would
select pathways thermodynamically more favorable to this condition, which produce less hydrogen
resulting in less CH4 formed per unit of feed ingested. It is possible that for the DMI ranges observed
50
in the present study, reductions in CH4 yield (g/kg of DMI) have compensated the greater DMI,
decreasing daily CH4 emissions. Our results indicate reductions around 20% for emission intensity
(g/kg of milk yield) and CH4 yield when adequate grazing management was adopted (LI95% pre-
grazing target), while Wims et al. (2010) and Muñoz et al. (2016) reported reductions of 10% for
temperate swards. It is worth mentioning that CH4 emission intensity of LI95% was 16.8 g of CH4/kg of
milk yield, lower than results reported in temperate pastures (18.8 g of CH4/kg of milk yield), and the
methane yield of LI95% was similar to results obtained on temperature pastures (20.2 vs. 21.5 g of
CH4/kg of DMI, values from Wims et al., 2010; Enriquez-Hidalgo et al., 2014; Muñoz et al., 2016).
These results highlight the need to review the historical concept of tropical grasses having low herbage
quality when managed under tight sward monitoring (Stobbs, 1975; Sollenberger and Burns, 2001).
Milk yield outputs per hectare increased between 40 and 50% by simply changing the pre-
grazing SSH of elephant grass from ≈135 cm to 100 cm. Greater milk productivity was achieved by
increased stocking rate (+33%) and milk yield per cow (+15%) when LI95% was adopted. Greater milk
yields in grazing dairy farms have been usually associated with the provision of additional feed (i.e.
increased nitrogen rates onto pastures, external supplementary feed inputs; Ramsbottom et al., 2015;
Macdonald et al., 2017). However, in addition to economic investments, both techniques are
associated with environmental issues, such as increased GHG emissions, water and land degradation
(Foley et al., 2011; Vogeler et al., 2013; Foote et al., 2015). Enteric CH4 emitted per hectare was
greater for lower pre-grazing SSH and it was a function of the higher stocking rates resulting from this
grazing strategy. However, this result has a small relative importance when milk yield per hectare is
considered. The 51% increase in milk productivity overcame the 29% increase in enteric CH4
emissions per hectare for the LI95% grazing management, and thereby determined a 16% mitigation of
the main greenhouse gas from pasture-based dairy systems (Crosson et al., 2011; Guerci et al., 2013;
Aguirre-Villegas et al., 2017).
In a context where the growing demand for food must be achieved through low
environmental footprint practices, our findings highlighted an opportunity to improve the efficiency of
tropical pasture-based dairy systems through optimization of ecological processes. Strategic grazing
allows for intensification that is not coupled with increase in external resources (i.e. fertilizer, external
supplements) but rather to efficient use of existing resources (i.e. solar radiation, rainwater, pasture,
fertilizer, supplement). In addition, strategic grazing management is a non cost and readily available
practice with easy adoption that enhances profitability of tropical pasture-based systems. The adoption
of strategic grazing in tropical pasture-based systems provides the opportunity to either increase farms'
total production, or use the extra land for food or forestry (further mitigating GHG emissions). The
decision regarding land use depends on governmental policy or market trends.
51
3.5. Conclusions
Strategic grazing management that reduces stem elongation in tropical forage grass swards
optimizes the processes inherent to plant growth (i.e. leaf accumulation and herbage nutritive value),
to plant animal interface (i.e. grazing efficiency and DMI), and animal (i.e. CH4 emission intensity and
CH4 yield), resulting in greater milk yield from the same area of land. Monitoring of SSH in tropical
pastures is a useful and reliable field tool that translates ecophysiological plant responses in a practical
manner to farmers towards sustainable intensification at pasture-based systems in the tropics.
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59
4. STRATEGIC GRAZING MANAGEMENT AND NITROUS OXIDE FLUXES
FROM PASTURE SOILS IN TROPICAL DAIRY SYSTEMS
Abstract
Greenhouse gases emissions are considered the most important among all environmental
issues of dairy farming systems. Nitrous oxide (N2O) has particular importance owing to its global
warming potential and stratospheric ozone depletion. The objective of this study was to investigate the
influence of two rotational grazing strategies characterized by two pre-grazing targets (95% and
maximum canopy light interception during sward regrowth; LI95% and LIMax, respectively) on N2O
fluxes from soil and milk productivity in a tropical dairy farming system based on elephant grass
(Pennisetum purpureum Schum. cv. Cameroon). The general hypothesis was that frequent defoliations
generated by the LI95% pre-grazing target would increase N2O fluxes, however this greater emission
would ultimately be compensated by greater milk productivity. Results indicated that LI95% pre-grazing
target provided more frequent defoliation than LIMax. Water-filled pore space (WFPS), soil and
chamber temperatures were affected by sampling period (P1 and P2). There was significant treatment ×
sampling period interaction on soil NH4+ concentration, and it was most likely associated with urinary-
N discharge. During P1, there was a greater urinary-N discharge for LI95% than LIMax (26.3 vs. 20.9 kg
of urinary-N/paddock) caused by higher stocking rate (10.0 vs. 8.3 cows/ha), which resulted in greater
N2O fluxes for LI95%. Inversely, during P2, the soil NH4+ and N2O fluxes were greater for LIMax than
LI95%. During this period, the greater urinary-N discharge (46.8 vs. 44.8 kg of urinary-N/paddock) was
likely associated with greater stocking period (1.88 vs. 1.46 days) for LIMax relative to LI95%, since both
treatments had similar stocking rate (9.5 vs. 9.9 cows/ha). Converting hourly N2O fluxes to daily basis
and relating to milk productivity, LI95% was 35% more efficient than LIMax (0.36 vs. 0.55 g N˗N2O/kg
milk.ha.day). In addition, LI95% pre-grazing target decreased 34% urea-N applied per milk yield per
hectare (0.57 vs. 0.86 g urea-N/kg milk.ha.day). Strategic grazing management represented by the
LI95% pre-grazing target allows for intensification of tropical pasture-based dairy systems enhancing
milk productivity and decreasing N-N2O emitted per kg of milk.
Keywords: Canopy light interception; Nitrous oxide fluxes; Grazed soils; Soil nitrogen; Land-use
improvement; Elephant grass
4.1. Introduction
Greenhouse gases (GHG) emissions are considered the most important among all
environmental issues of dairy farming systems (O’Brien et al., 2012; Guerci et al., 2013; Gregorini et
al, 2016). Nitrous oxide (N2O) has particular importance owing to its global warming potential (265-
298 times greater than carbon dioxide; Myhre et al., 2013) and stratospheric ozone depletion
(Ravishankara et al., 2009; IPCC, 2014). It is the main GHG from the soil and the second most
representative among all GHG, ranging from 15 (housed) to 25% (pasture-based) of total GHG
emissions in dairy farming systems (Aguirre-Villegas et al., 2017). Methane (CH4) and carbon dioxide
(CO2) from soils are proportionally less important than N2O in dairy farming systems (Jarvis et al.,
1995; de Klein et al., 2008; Aguirre-Villegas et al., 2017).
60
Nitrous oxide is formed through microbial transformation of nitrogen (N) compounds into
the soil, typically by incomplete denitrification or by nitrification (Wrage et al., 2001; Saggar et al.,
2013). Nitrous oxide fluxes are affected by a wide range of proximal and distal regulators, making its
regulation a very complex process (de Klein et al., 2008; Luo et al., 2017). Proximal soil factors
include mineral nitrogen (NH4+ and NO3
−) and organic carbon availabilities, moisture, pH,
temperature, and texture that, in turn, are affected by distal regulators such as rainfall or irrigation, soil
compaction, organic matter and N inputs (de Klein et al., 2008; Luo et al., 2017). Periods when soil
characteristics favorable to N2O production coincide are called “hot moments” (Luo et al., 2017). In
tropical conditions, these “hot moments” usually occur during spring and summer when pastures are
intensively growing owing to the abundance of solar radiation, rainfall, and N inputs.
Grazing management strategies can strongly affect the majority of distal regulators. It
determines ecophysiological plant processes such as herbage growth, senescence and decay (Da Silva
et al., 2009; Pereira et al., 2014; Pereira et al., 2015; Da Silva et al., 2015; Congio et al., 2018) that
strongly affect animal responses such as herbage intake (Congio et al., 2018), herbage losses by cattle
trampling (Carnevalli et al., 2006; Silveira et al., 2013; Congio et al., 2018), stocking rate (Voltolini et
al., 2010; Gimenes et al., 2011; Congio et al., 2018), excreta spatial distribution (White et al., 2001;
Auerswald et al., 2010), and N load into pastures (Vibart et al., 2017). These factors, in turn, modify
soil properties (i.e. bulk density, moisture, temperature, pH, aeration) (Warren et al., 1986; Silva et al.,
2003; Schmalz et al., 2013) that affect microbial community growth and activity (Bardgett et al., 1996;
Bardgett et al., 2001; Bardgett and Wardle, 2003) determining the intensity of processes associated to
N2O fluxes from soils (de Klein et al., 2008; Levine et al., 2011; Luo et al., 2017).
The majority of studies involving N2O flux from pasture soils have been addressed to assess
the effects of proximal factors on processes and emission factors in temperate conditions (Saggar et
al., 2013; de Klein et al., 2014; Barneze et al., 2015; Venterea et al., 2015; Gardiner et al., 2016;
Samad et al., 2016; Clough et al., 2017; Gardiner et al., 2017; van der Weerden et al. 2017; Luo et al.,
2018; Rex et al., 2018). The little information available for tropical pastures has also focused on
nitrous oxide fluxes related to proximal factors within urine patches (Barneze et al., 2014; Lessa et al.,
2014; Mazzetto et al., 2014; Mazzetto et al., 2015). There is no information available regarding N2O
fluxes from soils of tropical pasture-based dairy farming systems, as influenced by grazing
management strategies. In fact, farming scale studies are scarce even in temperate conditions.
Experimental approaches have shown that intensively managed grasslands are stronger sources of N2O
than extensively managed grasslands owing to greater inputs of N fertilizer and excreta (Smith et al.,
2001; Flechard et al., 2007; Rafique et al., 2011). However, they have not accounted for animal
outputs that are usually greater in intensively managed systems and could compensate the higher N2O
fluxes. The objective of this study was to investigate the influence of two rotational grazing strategies
characterized by two pre-grazing targets (95% and maximum canopy light interception during sward
regrowth; LI95% and LIMax, respectively) on N2O fluxes from soil and milk productivity in a tropical
61
dairy farming system based on elephant grass (Pennisetum purpureum Schum. cv. Cameroon). The
general hypothesis was that frequent defoliations generated by the LI95% pre-grazing target would
increase N2O fluxes, however this greater emission would ultimately be compensated by greater milk
productivity.
4.2. Material and Methods
All procedures for this study were approved by the Animal (15.5.1246.11.2) and
Environment Ethics Committees (17.5.999.11.9) at the University of São Paulo, College of
Agriculture “Luiz de Queiroz” (USP/ESALQ).
4.2.1. Study site
The experiment was conducted in Piracicaba, SP, Brazil (22°42′S, 47°38′W and 546 a.s.l.)
on a rainfed, non-irrigated elephant grass (Pennisetum purpureum Schum. cv. Cameroon) pasture
established in 1972 in a high fertility Eutroferric Red Nitossol (Pereira et al., 2014). The climate is
sub-tropical with dry winters and 1328 mm average annual rainfall (CEPAGRI, 2012). The lowest and
highest mean temperatures were recorded in July (19.7 °C) and December (27.1 °C), respectively. The
greatest accumulated rainfall was observed from late spring to summer (1090 mm from November
2015 to March 2016), and the lowest from winter to early spring (356 mm from June to October
2015). Soil properties (0˗10 cm) at the beginning of each sampling period are presented in Table 1.
Table 1. Soil properties (0˗10 cm) at the beginning of each sampling
period (P1 and P2)
Clay Sand Silt Bulk Density pH OM NH4+ NO3
-
g/kg g/cm3 CaCl2 g/dm3 mg/kg dry soil
P1*
LI95% 502 168 330 1.31 5.1 43 283.4 5.0
LIMax 478 172 350 1.30 5.0 46 113.1 8.8
P2**
LI95% 511 179 310 1.32 5.1 43 76.6 20.6
LIMax 487 193 320 1.44 5.1 53 318.4 1.4 * Sampling on 01/08/2016 ** Sampling on 02/25/2016
4.2.2. Treatments and experimental design
The two treatments were pre-grazing targets of either 95% or maximum canopy light
interception during regrowth (LI95% and LIMax, respectively). The 2.5 ha experimental area was divided
into two farmlets of 18 paddocks (686 m2 on average) each, according to a randomized complete block
62
design, with six replications. The slope and chemical soil characteristics were considered as blocking
criteria.
Treatments based on canopy light interception resulted in contrastant sward structures and
determined pre-grazing sward surface heights (SSH) of 100 cm (LI95%) and 135 cm (LIMax). For both
pre-grazing SSH, the herbage depletion level (post-grazing height) corresponded to 50% of the pre-
grazing SSH as a means to maintain high short-term rates of herbage intake (Fonseca et al., 2012;
Carvalho, 2013). Treatments were allocated to the farmlets in mid-January 2015 after grazing and
mowing at 45-cm for standardization. During 11-months prior to field measurements, each farmlet
was adapted to its respective grazing management strategy. Paddocks were rotationally grazed by 10-
13 dairy cows in order to keep grazing management targets. The adaptation period was necessary to
adapt sward structure to treatments and to identify the corresponding pre-grazing SSH for the LI pre-
grazing targets used (LI95% and LIMax) (Congio et al., 2018).
Measurements were performed after the adaptation period during the second rainy season
from December 2015 to April 2016 (experimental period). A total of 215 kg N/ha (as urea, 45% of N)
was applied throughout the experimental period. Because grazing interval was not constant (as a
consequence of experimental design), the total amount of N to be applied was divided throughout the
experimental period (119 days) and a daily rate of N fertilizer was calculated. The amount of N
applied per paddock after each grazing was proportional to the length of the corresponding rest period
(daily rate × rest period), ensuring similar N fertilizer application to both treatments at the end of the
experimental period (Da Silva et al., 2017).
4.2.3. Soil flux measurements, analysis and flux calculation
Soil gaseous fluxes were measured using the non-ventilated closed static chamber
methodology updated by the Global Research Alliance on Agricultural Greenhouse Gases (de Klein
and Harvey, 2015). Gas samples were collected during two sampling periods throughout the
experimental period (P1 = 01/08/2016 to 01/22/2016 and P2 = 02/25/2016 to 03/10/2016).
Measurements were made at post-grazing, immediately after N fertilization with ten chambers
randomly placed 5-cm into bare ground in each paddock.
Chambers of 17.67 L were made of PVC, composed of a base (30 cm diameter and 20 cm
height) plus cap (30 cm diameter and 10 cm height), and were insulated with thermal blanket to avoid
heating during sampling (de Klein et al., 2014; Di et al., 2016). Gas samples were collected
immediately after chamber closing, and at 30 and 60 min. Samples were collected from cap sampling
port using 20 mL plastic syringes (Becton Dickinson, Franklin Lakes, NJ, EUA) and precision glide
needles (0.8 × 40 mm; BD), and injected into sealed and evacuated 10 mL glass sample vials. Gas
sampling started 24 h after chamber placement to allow soil microbial community to stabilize and
63
minimize overestimation or underestimation of emissions (Chiavegato et al., 2015). Samples were
performed during five consecutive days, and then every five days until the 15th day after fertilization.
Chambers were removed after P1 evaluation and re-placed at the beginning of P2. All samples were
collected from 8 to 9:15 am (Alves et al., 2012) and analyzed using gas chromatography at the
Laboratory of Analytical Chemistry (Embrapa Pecuária Sudeste, São Carlos, SP, BRA).
The chromatograph GC-2014 (Shimadzu, Columbia, MD, EUA) was equipped with electron
capture detectors (ECD) at 325 ºC (column HayeSep T 80/100) for N2O and flame ionization detectors
(FID) at 250 ºC for CO2 (column HayeSep T 80/100). Calibration curves were established using
standard certified gases for CO2 (260.2 ± 0.68%; 508.3 ± 0.61%, 1058 ± 1.37% and 1995 ± 0.54%
ppm) and N2O (257.3 ± 0.76%; 502.8 ± 0.69%, 999.5 ± 1.77% and 2328 ± 4.84% ppt). Gas
chromatography outputs were analyzed to determine linearity from 0 to 60 min. A strong linear
relationship was observed for N2O (r2 = 0.88) and the hourly gas fluxes were calculated according to
the increase of gas concentration into the head space over sampling time (de Klein et al., 2014; Luo et
al., 2018):
𝐺𝑎𝑠 𝑓𝑙𝑢𝑥 = 𝛿𝐺𝑎𝑠
𝛿𝑇 ×
𝑀
𝑉𝑚 ×
𝑉
𝐴 (1)
where δGas is the increase in head space gas concentration overtime (µL/L); δT is the
enclosure period (hours); M is the molar weight of N in N2O; Vm is the molar volume of gas at the
sampling temperature (L/mol); V is the headspace volume (m3); and A is the area covered (m2). Fluxes
were corrected for chamber bias to account for suppression of the surface-atmosphere concentration
gradient (Venterea, 2010) and hourly fluxes were assumed to represent mean daily fluxes (de Klein et
al., 2014).
4.2.4. Weather and ancillary measurements
Atmospheric pressure, ambient temperature, and rainfall were daily monitored at the weather
station located at 50 m from the experimental area. Soil and head-space temperature were recorded for
each chamber in each timepoint with a digital thermometer (TE˗300, Instrutherm, São Paulo, SP,
BRA). At the first day of each sampling period, four cores of each paddock were collected to
determine soil bulk and soil particle densities. During the first day of sampling, additional soil samples
were taken at 5-cm depth adjacent to each chamber in order to determine soil nitrate (NO3−) and
ammonium (NH4+). Soil N was extracted for one hour with 2 M KCl, filtered (Whatman 42) and
samples were analyzed for mineral N concentration by flow injection analysis (ASIA; Ismatec, Zürich,
Switzerland). At each sampling day prior to gas collection, soil samples were taken (0˗5 cm) from
adjacent area of each chamber for soil gravimetric moisture determination (24 h at 105 ºC).
Volumetric water contents were calculated by multiplying gravimetric water contents by soil bulk
64
density and soil water-filled pore-space (WFPS) was calculated by dividing volumetric water content
by total soil porosity (de Klein et al., 2014; Luo et al., 2018).
4.2.5. Statistical analysis
Analysis of variance was performed using the Mixed Procedure of SAS (SAS 9.3; SAS
Institute Inc., Cary, NC). Different structures of the variance˗covariance matrices were tested, and
variance components matrix was chosen as the best fit for the majority of variables based on the
Bayesian Information Criterion. The model included fixed effects of treatment, sampling period, and
their interaction, and random effect of chamber. Chambers were considered experimental units and
sampling periods were treated as repeated measures. Soil temperature, air temperature, WFPS, soil
NH4+ and soil NO3
− were tested as explanatory variables. Means were calculated using the LSMEANS
statement, compared using the Student's t-test and differences were declared significant at P ≤ 0.05.
For N2O fluxes, WFPS was used as covariate. To better understand the relations among dependent
variables, a principal component analysis (PCA) was performed using a data set comprised of N2O
fluxes, soil NH4+, soil NO3
−, soil temperature, chamber temperature, and WFPS. Principal components
scores were submitted to ANOVA to describe and interpret the effects of treatment and periods
(Jolliffe, 2002).
4.3. Results
4.3.1. Weather conditions
Weather conditions during the two sampling periods are presented in Figure 1. Air
temperature ranged from 16.6 to 35.2 ºC with average of 25.7 ºC during P1 (Figure 1A). Similarly,
during P2, air temperature ranged from 18.4 to 33.3 ºC with average of 24.9 ºC (Figure 1B). Average
soil temperatures were 22.7 and 24.7 ºC for P1 and P2, respectively. Accumulated rainfall was 199 mm
during P1 and 106 during P2 (Figures 1A and 1B, respectively).
65
Figure 1. Air and soil (0-5 cm) temperatures (ºC) and rainfall (mm) during sampling periods P1 (A)
and P2 (B) at the study site (Jan-Mar 2016).
4.3.2. Soil parameters
Water-filled pore space, soil and chamber temperatures were affected by sampling period (P
< 0.01) being greater for P2 than P1 (Table 2). Both soil NH4+ and NO3
− concentrations were not
affected by treatment or sampling period (P > 0.05), however there was a significant interaction
between treatments and sampling period for soil NH4+ (P = 0.0006) and a trend for soil NO3
− (P =
0.0725). During P1, there was an effect of LI pre-grazing targets on soil NH4+, with greater values
observed for LI95% than LIMax; however, during P2 soil NH4+ was greater for LIMax than LI95% (P <
0
5
10
15
20
25
30
35
40
45
50
0
5
10
15
20
25
30
Ra
infa
ll (
mm
)
Tem
per
atu
re (
ºC)
Days
Rainfall Air temperature Soil temperatureA
0
5
10
15
20
25
30
35
40
45
50
0
5
10
15
20
25
30
Rain
fall
(m
m)
Tem
per
atu
re (
ºC)
Days
Rainfall Air temperature Soil temperature
B
66
0.05). Water-filled pore space and rainfall patterns for both periods are presented in Figure 2. Days
with no rainfall markedly decreased WFPS during the beginning and the end of P1 (Figure 2A). There
was no effect of LI pre-grazing target on WFPS during P1 (P = 0.9967; Figure 2A), but the effect was
significant during P2 (P = 0.05; Figure 2B).
Table 2. Water-filled pore space (WFPS), soil temperature,
chamber temperature, soil ammonium and nitrate concentrations
from soil established with elephant grass subjected to strategies of
rotational stocking management (LI95% or LIMax) during sampling
periods P1 (01/08/2016 to 01/22/2016) and P2 (02/25/2016 to
03/10/2016) (n = 10)
Period
SEM1 P-value
1 2 Trt2 Per3 Trt×Per
WFPS, %
LI95% 77.8 94.5 1.57 0.1654 <0.0001 0.1672
LIMax
Soil Temp., ºC
LI95% 23.7 24.9 0.11 0.4125 <0.0001 0.4631
LIMax
Chamber Temp., ºC
LI95% 22.6 23.7 0.14 0.7344 <0.0001 0.8221
LIMax
NH4
+, mg/kg dry soil
LI95% 283.4 Aa 76.6 Bb 69.44 0.8771 0.4915 0.0006
LIMax 21.4 Bb 318.4 Aa
NO3
−, mg/kg dry soil
LI95% 5.0 Aa 20.6 Aa 6.18 0.2218 0.5126 0.0725
LIMax 8.8 Aa 1.4 Ba Means followed by the same capital letter in columns and the lower case letter in rows do not differ (P > 0.05)
1Standard error of the mean 2Treatment effect 3Sampling period effect
67
Figure 2. Water-filled pore space (WFPS) and rainfall (mm) during sampling periods P1 (A) and P2
(B) at the study site (Jan-Mar 2016). 1Standard error of the mean
4.3.3. Nitrous oxide fluxes
Nitrous oxide fluxes were not affected by pre-grazing targets (P = 0.9975), however they
were strongly affected by sampling period and WFPS (P < 0.01; Table 3). On average, N2O fluxes
were greater during P1 than P2 (375.9 vs. 134.5 µg N˗N2O/m2.h; P < 0.01). There was a significant
interaction between treatments and sampling period (P = 0.004). During P1, N2O fluxes were greater
for LI95% (P = 0.0405) and during P2 fluxes were greater for LIMax (P = 0.0414). Nitrous oxide fluxes
0
5
10
15
20
25
30
35
40
45
50
0
10
20
30
40
50
60
70
80
90
100
Ra
infa
ll (
mm
)
WF
PS
(%
)
Days
Rainfall LI95% LIMax
LI95% = 77.8
LIMax = 77.8
P = 0.9967
SEM1 = 2.22
A
0
5
10
15
20
25
30
35
40
45
50
0
10
20
30
40
50
60
70
80
90
100
Ra
infa
ll (
mm
)
WF
PS
(%
)
Days
Rainfall LI95% LIMaxB
LI95% = 91.4
LIMax = 97.5
P = 0.05
SEM1 = 2.22
68
across sampling periods and days are shown in Figure 3. Across days, there were no differences on
N2O fluxes during P1 (P > 0.05; Figure 3A). During P2, four of seven days had greater N2O fluxes for
LIMax than LI95% (P < 0.05; Figure 3B).
Table 3. Nitrous oxide fluxes (µg N˗N2O/m2.h) from soil established
with elephant grass subjected to strategies of rotational stocking
management (LI95% or LIMax) during sampling periods P1 (01/08/2016 to
01/22/2016) and P2 (02/25/2016 to 03/10/2016) (n = 10)
Period
SEM1 P-value
1 2 Trt2 Per3 Trt×Per WFPS4
LI95% 432.8 Aa 77.6 Bb 40.61 0.9975 <0.0001 0.004 <0.0001
LIMax 319.1 Ba 191.5 Ab Means followed by the same capital letter in columns and the lower case letter in rows do not
differ (P > 0.05)
1Standard error of the mean 2Treatment effect 3Sampling period effect 4Water-filled pore space effect
69
Figure 3. Nitrous oxide fluxes (µg N˗N2O/m2.h) derived from soil established with elephant grass
subjected to strategies of rotational stocking management (LI95% or LIMax) during sampling periods P1
(A) and P2 (B).
4.3.4. Principal component analysis
Principal component analysis generated six principal components, however, only the first
two were explored because had eigenvalues greater than 1 (Kaiser criteria; Jolliffe, 2002) and
accounted for 71.8% of the total variance in N2O fluxes (Table 4). The first principal component
(PC1) explained 49% of the total variance and indicated high positive scores for N2O fluxes and
WFPS, and high negative scores for soil and chamber temperatures. Analysis of variance on PC1
35.996.2
1066.9
632.6
482.9
218.6
31.325.1
33.6
708.2
549.3
278.9
147.5 39.7
0
200
400
600
800
1000
1200
1400
N2O
flu
x (
µg
N-N
2O
/m2.h
)
Days
LI95% LIMax
ns ns ns ns nsns ns
A
221.7177.1
83.0
176.7
95.0
25.4 27.7
336.5 378.1
284.5
435.5
361.7
131.1
65.7
0
200
400
600
800
1000
1200
1400
N2O
flu
x (
µg
N˗N
2O
/m2.h
)
Days
LI95% LIMax
ns ns ns** *** ** ***
B
70
scores showed a significant effect of sampling period (P < 0.01). The second principal component
(PC2) accounted for 22.8% of the total variance and showed high positive score for soil NH4+ and high
negative score for soil NO3− contents. Analysis of variance on PC2 scores showed a significant effect
of treatment × sampling period interaction (P = 0.0015).
Table 4. Coefficients of principal components
based on the correlation matrix for N2O
fluxes, soil NH4+ and NO3
−, soil and chamber
temperatures, and water-filled pore space
from soil established with elephant grass
subjected to strategies of rotational stocking
management (LI95% or LIMax)
Variables PC1 PC2
N2O fluxes 0.49 -0.08
Soil NH4+ 0.13 0.67
Soil NO3− 0.00 -0.70
Soil temperature -0.48 0.19
Chamber temperature -0.49 0.07
Water-filled pore space 0.52 0.15
Eigenvalue 2.94 1.37
% of variation explained 49.0 22.8
ANOVA P-value
Trt1 0.1149 0.6239
Per2 <0.0001 0.2950
Trt×Per 0.6934 0.0015 1Treatment effect 2Sampling period effect
4.4. Discussion
The grazing management strategies used in this study provided contrastant pre- and post-
grazing SSH that affected grazing interval and ultimately the number of grazing cycles. For LIMax, pre-
and post-grazing SSH were 135 and 64 cm, respectively, which resulted in an average grazing interval
of 32 days and 3.5 grazing cycles during the experimental period (Congio et al., 2018). On the other
hand, for LI95%, pre- and post-grazing SSH were 100 and 50 cm, respectively, which resulted in an
average grazing interval of 21 days and 5.6 grazing cycles (Congio et al., 2018). Considering
adaptation and experimental periods (from January 2015 to April 2016) there were 9.3 grazing cycles
for LIMax and 14.1 for LI95%, indicating greater frequency of defoliation on paddocks managed with the
LI95% target relative to those managed with the LIMax target. To keep the pre- and post-grazing targets,
stocking rate was 33% greater for LI95% than LIMax during the experimental period (9.3 vs. 7.0
cows/ha; Congio et al., 2018). These grazing conditions created different scenarios of intensification,
solely by changing pre-grazing targets (LI95% or LIMax). It is worthwhile to mention that the greater
stocking rate obtained in LI95% was supported by greater leaf accumulation and greater grazing
71
efficiency rather than increased N fertilizer input, usually applied in intensive temperate pasture-based
systems (Ramsbottom et al., 2015; Macdonald et al., 2017; Congio et al., 2018).
Emissions of N2O are a result of soil microbial nitrification and denitrification (de Klein and
Eckard, 2008; Saggar et al., 2013). Both processes are mediated by soil properties such as mineral N
(i.e. NH4+ and NO3
−) and organic carbon availabilities, moisture, pH, temperature, and texture (de
Klein et al., 2008; Luo et al., 2017). In grazed pastoral soils, the factors pointed out as key drivers of
N2O fluxes are N inputs (i.e. urine patches and fertilizer) and WFPS (de Klein et al., 2008; Luo et al.,
2017). Nitrous oxide fluxes and soil NH4+ varied with LI pre-grazing target × sampling period
interaction, while a trend was observed for the effect of NO3−. On the other hand, the variables related
to weather (i.e. WFPS, soil and chamber temperatures) varied only with sampling period. Most studies
have indicated that high N2O emissions are usually associated with anaerobic soils with enough NO3−
supply suggesting that denitrification is the main process responsible for N2O emissions (de Klein and
Eckard, 2008; de Klein et al., 2008). However, on excessively saturated soils with higher WFPS (i.e.
optimal conditions for denitrification), as observed in P2, denitrification is complete and results in
greater N2:N2O ratio (Bolan et al., 2004; de Klein et al., 2008). Although the accumulated rainfall was
greater during P1 (199 mm) than P2 (106 mm), the WFPS was constantly greater throughout P2 than P1.
These results are likely associated with better rainfall distribution during P2, where there were 80% of
rainy days, while during P1 there were just 47% of rainy days.
The WFPS oscillation throughout P1 followed the rainfall pattern. At day 3 (1/10/16), a 39
mm rainfall increased WFPS from around 50 to more than 90% (Figure 2A), driving N2O fluxes up to
1000 µg N˗N2O/m2.h (Figure 3A). Thenceforward, the WFPS was kept above 90% until day 10
(1/17/16) and N2O fluxes decreased likely because of low oxygen availability that may have favored
complete denitrification and N2 production (Bolan et al., 2004; de Klein et al., 2008). Throughout P2,
the more uniform rainfall regime maintained WFPS above 90% with little oscillation until day 15
(3/10/16; Figure 2B), and N2O fluxes were kept moderate during the first half of P2, decreasing at the
end of the period (Figure 3B). Studies have reported that the peak of N2O emissions occurs at WFPS
values around 60-80%, when simultaneous nitrification and denitrification were at maximum levels
(Davidson, 1992; Rafique et al., 2011). Above this WFPS range, denitrification is the main source of
N2O and under excessively anaerobic conditions, N2:N2O ratio remains greater (Bolan et al., 2004; de
Klein et al., 2008; Rafique et al., 2011). The results of PCA pointed to an interaction among the
driving factors regulating N2O fluxes from soil. The first principal component indicated that
environmental factors (i.e. WFPS, soil and chamber temperatures) were determinants of N2O
emissions and explained 49% of the whole dataset variability. Principal component two showed that
factors related to LI pre-grazing targets (i.e. soil NH4+ and NO3
−) had the highest scores and accounted
for 22.8% of total variance. Flechard et al. (2007) also reported that weather factors explained half of
the total variability in their N2O flux dataset of ten sites for three years across Europe. Analysis of
variance on PC1 and PC2 scores corroborated the results from the analysis of variance, where
72
environmental factors showed significant effect for sampling period, as observed in PC1, and
treatment related factors showed significant effect for the LI pre-grazing target × sampling period
interaction, as observed in PC2.
Both soil NH4+ and NO3
− represented the concentration immediately after urea fertilization at
day one, and therefore indicate N availability at the beginning of each sampling period. For both LI
pre-grazing targets, a total of 215 kg N/ha was applied throughout the experimental period. However,
this amount was divided in 3.5 and 5.6 instalments for LIMax and LI95%, respectively. Therefore, the N
inputs from urea fertilizer immediately before N2O sampling were greater for LIMax than LI95% during
P1 (75 vs. 44 kg N/ha) and P2 (111 vs. 57 kg N/ha). However, there was a significant treatment ×
sampling period interaction on soil NH4+ concentration, most likely associated with urinary-N
discharge. During P1, there was a greater urinary-N discharge for LI95% than LIMax (26.3 vs. 20.9 kg of
urinary-N/paddock) caused by higher stocking rate (10.0 vs. 8.3 cows/ha), which resulted in greater
N2O fluxes for LI95%. Inversely, during P2, the soil NH4+ and N2O fluxes were greater for LIMax than
LI95%. During this period, the greater urinary-N discharge (46.8 vs. 44.8 kg of urinary-N/paddock) was
likely associated with greater stocking period (1.88 vs. 1.46 days) for LIMax relative to LI95%, since both
treatments had similar stocking rate (9.5 vs. 9.9 cows/ha). These results are in agreement with most
studies that have reported urine patches as the main source of N2O from grazed pasture soil mainly by
providing highly localized concentrations of available N, ranging from 200-2000 kg N/ha, associated
with increased moisture and temperature conditions (Selbie et al., 2015; Luo et al., 2018).
Dairy farming systems based in temperate pastures are usually more intensive than tropical
pasture-based dairy systems (Congio et al., 2018). Temperate forage crops were deeply studied and the
understanding of their ecophysiology allowed for better use by farmers through adoption of adequate
grazing management strategies, ensuring high milk productivity. The intensification of such systems is
usually coupled with extra inputs of N fertilizer to boost forage growth or external supplementary
feed, both aiming at increased stocking rate (Ramsbottom et al., 2015; Macdonald et al., 2017). In the
tropics, dairy farming systems usually have low N inputs and adopt inadequate grazing management
strategies resulting in low milk productivity. Therefore, the intensification of tropical pasture-based
dairy systems is possible through adoption of adequate grazing strategies rather than extra N inputs or
additional supplements, provided that minimum soil fertility to meet plant nutritional demand is
ensured. The results indicated the opportunity to increase milk productivity in 52% (170 and 112
kg/ha.day for LI95% and LIMax, respectively; Congio et al., 2018) only with adoption of strategic
grazing management (i.e. LI95% pre-grazing target).
Experimental approaches have shown that intensively managed pastures are greater sources
of N2O than extensively managed pastures (Flechard et al., 2007; Rafique et al., 2011). Rafique et al.
(2011) reported that frequently grazed sites that applied 400 kg of N/ha emitted two times more N2O
compared to less frequently grazed sites that used around 300 kg of N/ha. However, in their study,
intensively managed systems were generated through greater inputs of N fertilizer. In the present
73
study, the more intensive grazing strategy was obtained through optimization of ecological processes
rather than additional inputs of N fertilizer (Congio et al., 2018). Although urinary-N excretion has
increased soil NH4+ and ultimately N2O fluxes during P1 for LI95%, during P2 the urinary-N excretion
and N2O fluxes were greater for LIMax counterbalancing the emissions for the entire experimental
period (255 µg N˗N2O/m2.h; P = 0.9975). Converting hourly N2O fluxes to daily basis and relating to
milk productivity, LI95% was 35% more efficient than LIMax considering emissions for the entire period
(0.36 vs. 0.55 g N˗N2O/kg milk.ha.day). Even during P1, when N2O fluxes were greater for LI95% than
LIMax, LI95% emited less N˗N2O/kg of milk.ha.day than LIMax (0.61 vs. 0.68 g N˗N2O/kg milk.ha.day).
In addition, strategic grazing management decreased 34% urea-N applied per milk yield per hectare
(0.57 vs. 0.86 g urea-N/kg milk.ha.day).
In a context where there is concern about the intensification of temperate pasture-based dairy
systems through greater N fertilizer inputs, these findings highlight an opportunity to improve the
efficiency of tropical pasture-based dairy systems through optimization of ecological processes.
Strategic grazing allows for intensification that is not coupled with increases in inputs of external
resources (i.e. fertilizer, external supplements) but rather with efficient use of existing resources (i.e.
solar radiation, rainwater, pasture, fertilizer, supplement). Congio et al. (2018) have shown that
strategic grazing management might reduce approximately 20% of enteric CH4 emission intensity and
CH4 yield of dairy cows in tropical pasture-based systems. Carbon footprint in dairy farming systems
is often dependent on emissions of enteric CH4 and N2O but also on carbon sequestration by forage
crops with increase in soil organic carbon. Abdalla et al. (2018) revealed that the impact of grazing
intensity on soil organic carbon is strongly climate dependent, and moist-warm regions present
different responses than dry-warm, dry-cold and moist-cold climates. The authors highlighted that C4
grass species under high grazing intensities in moist-warm regions are more likely to increase soil
organic carbon through enhanced plant turnover (i.e. root and litter) and excreta distribution than C4
under low grazing intensity. Then, the intensification of tropical pasture-based dairy farms through
strategic grazing management uncoupled to N fertilizer increases could be a strategy for GHG
mitigation. In addition, strategic grazing management is a noncost and readily available practice with
easy adoption that enhances profitability of tropical pasture-based systems.
4.5. Conclusions
Nitrous oxide fluxes from grazed pastoral soils in moist-warm conditions is a very complex
process regulated by environmental conditions and soil nitrogen availability. The central hypothesis
that frequent defoliation provided by the LI95% pre-grazing target would result in greater N2O fluxes
from soil than less frequent defoliation (i.e. LIMax) was not confirmed. However, these results
associated with those of Congio et al. (2018) highlight that it is possible to intensify tropical pasture-
74
based dairy systems through the adoption of adequate grazing strategies rather than extra N fertilizer
inputs or additional supplements, as is usually pointed out for temperate grazing systems. This
indicates the opportunity to significantly enhance milk productivity from tropical pasture-based
systems using strategic grazing management (LI95%; Congio et al., 2018) and decrease 35% the N-N2O
emitted per kg of milk. However, further farm-scale studies are necessary for a wide range of tropical
grazed soils and dairy farming systems, associating not only GHG sources, but also the carbon
sequestration by pasture soils, and milk productivity to achieve more accurate estimates of carbon
balance and then to support mitigation plans by policy makers.
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5. EFFECTS OF TIMING OF PADDOCK ALLOCATION ON MILK YIELD AND
ENTERIC METHANE EMISSIONS FROM DAIRY COWS
Abstract
Dairy products are major components of the human diet. Pasture-based systems are
important milk suppliers to dairy industry in temperate and tropical climates and thereby will play
relevant role to support the growing demand. However, this additional milk supply must be obtained
through higher yields resulting from intensification of existing farming systems using environmentally
friendly and economically profitable strategies towards sustainable intensification. The objective of
this study was to investigate the influence of timing of paddock allocation (AM or PM) on the
nutritive value of rotationally managed elephant grass (Pennisetum purpureum Schum. cv. Cameroon),
and the dry matter intake (DMI), milk yield, milk composition, and enteric methane (CH4) emissions
of Holstein × Jersey dairy cows. The hypothesis was that new paddock allocation to dairy cows in the
afternoon, when herbage has greater nutritive value, increases nutrient intake and milk yield, and
reduces enteric CH4 emissions per kg of milk, relative to paddock allocation in the morning. Herbage
sampled in the afternoon had greater dry matter, soluble carbohydrates, starch, and non-fibrous
carbohydrate/protein ratio, and lesser neutral-detergent fiber and acid-detergent fiber concentrations.
There was no treatment effect on milk yield. However, protein and casein yields tended to be greater
for PM than AM. Milk urea nitrogen was greater for cows grazing paddocks allocated during the
morning relative to those allocated in the afternoon. The timing of paddock allocation did not affect
DMI, daily enteric CH4 emission, and enteric CH4 per kg of milk. The results ratify the general
understanding of diurnal variation in herbage chemical composition. However, the increase in nutritive
value of the afternoon relative to the morning herbage was not enough to increase DMI and milk yield,
or to decrease CH4 emission intensity by the dairy cows as hypothesized. The findings also indicate
that new paddock allocation during the afternoon can be a simple and useful grazing strategy that
results in greater N partitioning to protein yield, and lower excretion of urea N in milk.
Keywords: Timing of paddock allocation; Enteric methane emissions; Herbage quality; Diurnal
variation; Non-fibrous carbohydrate; Elephant grass
5.1. Introduction
Dairy products are major components of the human diet (Aguirre-Villegas et al., 2017).
Pasture-based systems are important milk suppliers to dairy industry in temperate (Chapman, 2016;
Macdonald et al., 2017) and tropical climates (Santos et al., 2014; de Souza et al., 2017) and thereby
will play relevant role to support the growing demand (Godfray et al., 2010; Conforti, 2011;
Alexandratos and Bruinsma, 2012). However, this additional milk supply must be obtained through
higher yields resulting from intensification of existing farming systems using environmentally friendly
(Tilman et al., 2002) and economically profitable (Foote et al., 2015; Gregorini et al., 2017) strategies
towards sustainable intensification (Godfray et al., 2010; Congio et al., 2018).
Several studies have reported diurnal variations in herbage chemical composition
(Lechtenberg et al., 1971; Orr et al., 1997; Ciavarella et al., 2000; Griggs et al., 2005; Gregorini et al.,
2006; Shewmaker et al., 2006; Gregorini et al., 2008; Morin et al., 2011). Such variations were
82
attributed to the balance among processes of plant photosynthesis, plant respiration, and plant
transpiration that results in greater non-fibrous carbohydrate (NFC) and dry matter (DM) accumulation
from dawn to dusk (Curtis, 1944; Lechtenberg et al., 1971). The increase in NFC and DM
concentrations mainly occur in the upper layers of the canopy (Delagarde et al., 2000), often diluting
fiber and nitrogen (N) concentrations (Gregorini, 2012; Vibart et al., 2017), and enhancing herbage
biomechanical properties (Gregorini et al., 2009) and digestibility (Burns et al., 2007; Pelletier et al.,
2010; De Oliveira et al., 2018). Therefore, temporal patterns of herbage intake, ingestive and digestive
behavior of grazing ruminants can be altered by timing of new strip or paddock allocation to grazing
animals in rotationally managed pastures (Gibb et al., 1998; Orr et al., 2001; Gregorini et al., 2006;
Gregorini et al., 2008; Abrahamse et al., 2009; Gregorini, 2012; Pulido et al., 2015; Vibart et al.,
2017). Although these studies have not reported that such modifications in the herbage chemical
composition can increase daily dry matter intake (DMI), Gregorini (2012) suggested that ruminants
moved to a new fresh paddock in the afternoon might increase their nutrient intake because of longer
and more intensive grazing during dusk, when herbage nutritive value is at its peak.
Enteric CH4 is the predominant source of greenhouse gases (GHG) emissions in dairy
systems (Crosson et al., 2011; Aguirre-Villegas et al., 2017) and represent more than 80% of total
GHG emissions in pasture-based farming systems (Guerci et al., 2013). According to Janssen (2010),
the nature and amount of feed (e.g. herbage chemical composition and DMI, respectively) are key
determinants of enteric CH4 emissions from ruminants. Modeling studies have shown possible
reductions on enteric CH4 emissions intensity (g/kg of milk) by dairy cows when herbage NFC
increases at the expense of fiber concentrations (Ellis et al., 2012), and Gregorini (2012) suggested the
need of field research to assess this hypothesis.
The objective of this study was to investigate the influence of timing of paddock allocation
(AM or PM) on the nutritive value of rotationally managed elephant grass (Pennisetum purpureum
Schum. cv. Cameroon), and the DMI, milk yield, milk composition, and enteric CH4 emissions of
Holstein × Jersey dairy cows. The hypothesis was that new paddock allocation to dairy cows in the
afternoon, when herbage has greater nutritive value, increases nutrient intake and milk yield, and
reduces enteric CH4 emissions per kg of milk, relative to paddock allocation in the morning.
5.2. Material and Methods
All procedures for this study were approved by the Animal (15.5.1246.11.2) and
Environment Ethics Committees (17.5.999.11.9) at the University of São Paulo, College of
Agriculture “Luiz de Queiroz” (USP/ESALQ).
83
5.2.1. Study site
The experiment was conducted from January to March 2017 in Piracicaba, SP, Brazil
(22º42’S, 47º38’W and 546 a.s.l.) on a rainfed, non-irrigated elephant grass pasture (Pennisetum
purpureum Schum. cv. Cameroon) established in 1972 in a high fertility Eutroferric Red Nitossol. The
climate is sub-tropical with dry winters and 1328 mm average annual rainfall (CEPAGRI, 2012). The
mean temperature and accumulated rainfall during the experiment were 24.5 ºC and 407 mm
respectively.
5.2.2. Treatments and experimental design
The 3.3 ha experimental area was divided up into two farmlets of 24 paddocks each (688 m2
on average), and managed using a common rotational grazing strategy with one day of occupation.
Pre- and post-grazing sward surface heights (SSH) were 100 and 55 cm, respectively, which were
found to optimize grazing efficiency and feeding value of elephant grass cv. Cameroon (Congio et al.,
2018). Paddocks were subjected to a period of 11 months prior to the beginning of the experiment
aiming to adapt sward structure to the grazing strategy used.
The two treatments corresponded to timings of herd allocation to a new paddock, either after
morning milking at 6:00 am (AM) or after afternoon milking at 4:00 pm (PM). The experimental
design was a randomized complete block, with eight replications, with slope and chemical soil
characteristics used as blocking criteria. Each paddock received 56 kg N/ha (as urea) during the
experiment splitted in 2 instalments. Fertilizer application was made soon after grazing. The
experimental period was divided in two sampling periods of 4 weeks each (P1 and P2) and
measurements were made during the last 7 days of each sampling period.
5.2.3. Plant measurements
The SSH was measured from ground level to top leaf horizon by 40 systematic readings,
using a stick graduated in centimeters (Pereira et al., 2015a; Congio et al, 2018). Pre-grazing herbage
mass was quantified in each grazing cycle on three rectangular samples collected randomly (0.94 m2
each). Herbage was clipped above the target post-grazing SSH, weighed fresh, and sub-sampled to
determine plant-part components by hand separation into leaf (leaf blades), stem (stems + leaf sheaths)
and dead material (Pereira et al., 2015b; Congio et al, 2018). Herbage allowance was calculated by the
relationship between pre-grazing herbage mass (above post-grazing SSH) and number of cows per day
(Pérez-Prieto and Delagarde, 2013; Congio et al, 2018).
Herbage samples to determine chemical composition were taken daily during the last 7 days
of each sampling period (P1 and P2) immediately before herd allocation to paddocks (6 am and 4 pm).
84
Herbage was clipped above target post-grazing SSH at ten randomized sampling sites per paddock,
homogenized, sub-sampled, freeze-dried, and ground through a 1-mm screen (Wiley Mill, Thomas
Scientific, Philadelphia, PA). Dry matter (DM) and ash concentrations were determined at 105 ºC for
24 h and 600 ºC for 4 h, respectively (AOAC International, 2005). Neutral-detergent fiber (NDF),
acid-detergent fiber (ADF) and lignin concentrations were determined sequentially (Van Soest et al.,
1991). Ether extract (EE) concentration was determined according to AOAC International (2005).
Total N concentration was determined by the Dumas combustion method using N analyzer (Leco FP-
2000 N Analyzer; Leco Instruments Inc., St. Joseph, MI, USA), and crude protein (CP) concentration
calculated as N × 6.25. Neutral-detergent insoluble crude protein (NDICP), acid detergent-insoluble
crude protein (ADICP), and soluble N concentrations were analyzed according to Licitra et al. (1996),
and N fractions were determined by methodology adapted from Sniffen et al. (1992). Soluble
carbohydrates in 80% ethanol-solution (SC) and starch concentrations were determined according to
Hall (2003).
5.2.4. Herd and feeding
Twenty Holstein × Jersey dairy cows averaging 461 ± 72 kg body weight (BW) and 2.83 ±
0.23 body corporal score (BCS) were used. Four weeks prior to the experiment, all cows were
managed in a single herd grazing elephant grass cv. Cameroon and receiving 6 kg (fresh basis) of
commercial concentrate daily. Cows were then stratified, grouped in pairs and allocated to 10 blocks
according to pre-experimental milk yield (18.6 ± 4.6 kg/d) and days in milk (102 ± 82 DIM). Within
pairs, cows were randomly assigned to treatments (AM and PM).
Concentrate meals were fed individually twice daily (4:30 am and 2:30 pm) before milking
(5 am and 3 pm) at a rate of 1 kg of concentrate/3 kg of milk (considering the average of each block).
The rate was established based on milk yield at the beginning of each period (Danes et al., 2013). The
concentrate meal was composed of fine ground corn (80%), soybean meal (15%) and mineral (5%),
with chemical composition as following: 86.8% of DM, 9.4% of ash, 13.6% of CP, 13.2% of NDF,
3.4% of ADF, 3.9% of EE and 59.9% of NFC.
5.2.5. Animal measurements
Cows were weighed and BCS recorded at the end of each sampling period (P1 and P2) during
three consecutive days (Edmonson et al., 1989). Milk yield was recorded daily with samples collected
in vials containing bronopol preservative pill and analyzed for fat, protein, lactose, milk solids and
milk urea nitrogen (MUN) using infrared procedures (MilkoScan FT+; Foss North America Inc., Eden
Prairie, MN).
85
Herbage intake was estimated from total fecal excretion and feed indigestibility. To estimate
total fecal excretion, titanium dioxide (TiO2) was dosed twice daily (20 g/cow per day) after
concentrate meals during 12 days. Fecal samples were collected from rectum following concentrate
meals during the last 5 days, dried in a forced-air drier at 55 °C for 72 h, ground through a 1-mm
screen (Wiley Mill, Thomas Scientific, Philadelphia, PA), and composited into one sample per
measurement period by cow. Titanium dioxide concentration in feces was determined according to
Myers et al. (2004). To determine the feed indigestibility, the indigestible NDF (iNDF) content of
herbage, concentrate, and fecal samples were estimated by 240 h in vitro incubation (Goeser and
Combs, 2009). Total fecal excretion, fecal excretion from concentrate, and herbage intake were
calculated according to de Souza et al. (2015).
Enteric CH4 emissions were estimated using sulfur hexafluoride (SF6) as tracer gas (Johnson
and Johnson, 1995). Pre-calibrated permeation tubes containing SF6 with known release rates (1.48 ±
0.32 mg/d) were placed into the rumen of each cow. Sampling apparatus included a PVC collection
canister (2.3 L), and adjustable halter containing stainless steel capillary tubing and brass connections.
Canisters were vacuumed to approximately ˗13.5 psi using a three-stage vacuum pump (Symbol,
Sumaré, SP, Brazil) and Druck DPI 705 digital manometer (GE Druck, South Burlington, VT, EUA)
and replaced daily just after the afternoon concentrate meal. Cows were adapted to the sampling
apparatus during 7 days prior to collection. Enteric CH4 emissions were measured at 24-hour intervals
over 7 consecutive days. Background SF6 and CH4 concentrations were determined using two
sampling apparatus placed daily in the field near the grazing herd. Prior to chromatograph
determination, canisters were pressurized to 1.3˗1.5 psi with ultrapure nitrogen 5.0, and pressures
recorded by Druck DPI 705 digital manometer (GE Druck, South Burlington, VT, EUA) in order to
calculate the dilution factor. Methane and SF6 concentrations were determined at the Laboratory of
Biogeochemistry and Tracer Gases Analysis (Embrapa Meio Ambiente, Jaguariúna, SP, BRA) using
gas chromatography (HP6890, Agilent, Delaware, USA). Chromatograph was equipped with flame
ionization detector (FID) at 280°C for CH4 (column megabore, 0.53 mm × 30 m × 15μm, Plot HP-
Al/M), and electron capture detector (ECD) at 300°C for SF6 (column megabore, 0.53 mm × 30 m ×
25 μm, HP-MolSiv), with two loops of 0.5 cm3 maintained at 80 °C attached to two six-way valves.
Calibration curves were established using standard certified gases for CH4 (4.85 ± 5%; 9.96 ± 1.65%
and 19.1 ± 3.44% ppm) and SF6 (34.0 ± 9.0; 91.0 ± 9.0 and 978.0 ± 98.0 ppt) (Westberg et al., 1998).
Daily methane emissions were calculated from collected SF6 and CH4 concentrations in the canisters
discounting background concentrations, and value of SF6 permeation tube release rate (Johnson and
Johnson, 1995).
86
5.2.6. Statistical analysis
Analysis of variance was performed using the Mixed Procedure (SAS 9.3; SAS Institute Inc.,
Cary, NC). Different structures of the variance-covariance matrices were tested and Bayesian
Information Criterion was adopted to select the best fit matrix. For plant parameters analysis, paddock
was considered as experimental unit, and for animal measurements, cow was considered as
experimental unit. Cows or paddocks blocks were considered random terms, and timing of new
paddock allocation, sampling period and their interactions were treated as fixed effects. Sampling
periods were treated as repeated measures. Means were calculated using the LSMEANS statement and
compared using the Student’s t-test. Differences were declared significant at P ≤ 0.05, and trends were
declared at P ≤ 0.10.
5.3. Results
5.3.1. Sward characteristics
Sward characteristics are presented in Table 1. Both pre- and post-grazing SSH did not vary
between treatments (P = 0.3124 and P = 0.8619, respectively). Post-grazing SSH was greater during
P1 than during P2 (P = 0.0127; 56.8 vs. 53.2 cm, respectively). There was no effect of timing of
paddock allocation on pre-grazing herbage mass (P = 0.6742), leaf-to-stem ratio (P = 0.9214) and
herbage allowance (P = 0.7694).
Table 1. Pre- and post-sward surface height (SSH) (cm), pre-grazing herbage mass
(kg of DM/ha), leaf:stem ratio and herbage allowance (kg of DM/cow.day) of
rotationally managed elephant grass cv. Cameroon with new paddocks allocated to
dairy cows either in the morning (AM) or in the afternoon (PM) (n = 8)
Item Treatments SEM1 P-value
AM PM Trt2 Per3 Trt×Per
Pre-SSH 101.6 100.4 0.80 0.2770 0.5879 0.7848 Post-SSH 55.7 54.2 1.18 0.2945 0.0127 0.9889
Pre-grazing herbage mass4 2270 2180 208.5 0.6742 0.6812 0.8185
Leaf:Stem ratio4 73.6 75.5 30.50 0.9214 0.9566 0.1092
Herbage allowance4 15.4 14.9 1.33 0.7694 0.8090 0.4176 1Standard error of the mean 2Treatment effect 3Sampling period effect 4Estimated above post-grazing SSH
5.3.2. Herbage chemical composition
Overall, herbage chemical composition differed between treatments (Table 2). Herbage
sampled in the afternoon had greater DM (P = 0.0003), SC (P < 0.01), starch (P < 0.01), NDICP (P =
87
0.0102), NFC/PROT ratio (P < 0.01), and lesser NDF (P = 0.0127) and ADF (P = 0.0053)
concentrations. There was no treatment effect on OM (P = 0.7879), lignin (P = 0.8951), EE (P =
0.6610), CP (P = 0.3324), soluble and degradable protein (P = 0.2106 and P = 0.7746, respectively),
and ADICP (P = 0.7893) concentrations. During P2, DM (P = 0.0007), OM (P < 0.0001), NDF (P <
0.0001) and NDICP concentrations (P < 0.0001) were greater, and EE (P = 0.0216) and degradable
protein (P = 0.0064) concentrations were lower relative to P1. There were no interactions between
treatments and sampling periods.
Table 2. Herbage chemical composition (% of DM) of rotationally managed elephant
grass cv. Cameroon with new paddocks allocated to dairy cows either in the morning
(AM) or in the afternoon (PM) (n = 7)
Item Treatments SEM1 P-value Periods P-value
AM PM 1 2
Dry matter 18.9 22.2 0.67 0.0003 19.1 22.1 0.0007 Organic matter 90.5 90.6 0.42 0.7879 89.1 92.0 <0.0001
Soluble carbohydrates 5.4 8.2 0.27 <0.0001 6.8 6.8 0.8955
Starch 1.5 2.9 0.13 <0.0001 2.3 2.1 0.1571
Neutral detergent fiber 61.8 60.0 0.56 0.0127 59.2 62.5 <0.0001
Acid detergent fiber 36.8 34.5 0.54 0.0053 35.7 35.6 0.9287
Lignin 3.4 3.4 0.16 0.8951 3.5 3.2 0.1237
Ether extract 3.1 3.0 0.08 0.6610 3.1 2.9 0.0216
Crude protein 17.6 17.1 0.42 0.3324 17.7 17.0 0.2506
Protein fractions2, % of CP
Soluble protein 26.0 24.0 1.11 0.2106 23.9 26.1 0.1869 Degradable protein 51.9 51.5 1.00 0.7746 53.8 49.5 0.0064
NDICP 16.0 17.5 0.37 0.0102 15.5 18.0 <0.0001
ADICP 6.6 6.6 0.31 0.7893 6.7 6.4 0.4115
NFC/PROT3 0.52 0.87 0.040 <0.0001 0.68 0.70 0.6847 1Standard error of the mean corresponds to both treatment and period effects 2Protein fractions adapted from Sniffen et al. (1992): Soluble protein (A+B1), Degradable protein (B2), NDICP (B3) and ADICP (C) 3NFC: (soluble carbohydrates + starch); PROT: (soluble protein + degradable protein)
5.3.3. Animal performance
The effects of timing of paddock allocation on animal performance are shown in Table 3.
There was no treatment effect on milk yield (P = 0.6618), fat yield (P = 0.9181), and milk solids yield
(P = 0.9240). However, protein (P = 0.0899) and casein (P = 0.0632) yields tended to be greater for
PM than AM. Timing of paddock allocation did not affect milk fat (P = 0.6285), milk protein (P =
0.2976), milk casein (P = 0.2346), and milk solids (P = 0.6760) concentration. Milk lactose
concentration (P = 0.0003) and MUN (P = 0.0032) were greater for cows grazing paddocks allocated
during the morning relative to those allocated in the afternoon.
88
Table 3. Milk yield (kg/d) and milk composition (% unless specified
otherwise) of dairy cows grazing rotationally managed elephant grass
cv. Cameroon with new paddocks allocated either in the morning
(AM) or in the afternoon (PM) (n = 10)
Item Treatments SEM1 P-value
AM PM Trt2 Per3 Trt×Per
Yield
Milk 17.2 17.4 1.20 0.6618 0.1023 0.8792 Fat 0.59 0.60 0.03 0.9181 0.2626 0.5564
Protein 0.55 0.58 0.02 0.0899 0.1802 0.3715
Casein 0.42 0.45 0.02 0.0632 0.1021 0.0979
Milk solids 2.1 2.1 0.11 0.9240 0.0422 0.8164
Composition
Fat 3.5 3.5 0.13 0.6285 0.9415 0.2179 Protein 3.2 3.3 0.10 0.2976 0.0373 0.1190
Lactose 4.6 4.4 0.06 0.0003 0.1067 0.1627
Casein 2.5 2.6 0.09 0.2346 0.1945 0.0295
Milk solids 12.3 12.2 0.25 0.6760 0.3864 0.8089
MUN4, mg/dL 14.6 13.0 0.46 0.0032 0.0538 0.217 1Standard error of the mean 2Treatment effect 3Sampling period effect 4MUN: milk urea nitrogen
5.3.4. Dry matter intake and enteric CH4 emissions
The effects of timing of paddock allocation on DMI and CH4 emissions are shown in Table
4. Herbage DMI (P = 0.97) and total DMI (P = 0.9578) did not differ between AM and PM treatments.
There was no treatment effect on daily enteric CH4 emission (P = 0.9350), and efficiencies of milk (P
= 0.6599), fat (P = 0.5750), protein (P = 0.3070), and milk solids (P = 0.6313) yield per g of CH4
emitted. Additionally, timing of paddock allocation did not affect CH4 yield (CH4/kg DMI; P =
0.3380).
Table 4. Daily dry matter intake (DMI) (kg of DM/cow) and enteric CH4
emissions of dairy cows grazing rotationally managed elephant grass cv.
Cameroon with new paddocks allocated to dairy cows either in the morning
(AM) or in the afternoon (PM) (n = 10)
Item Treatments SEM1 P-value
AM PM Trt2 Per3 Trt×Per
Daily DMI
Herbage 11.6 11.6 0.3862 0.97 0.27 0.002 Total 17.3 17.3 0.4494 0.9578 0.5276 0.0033
CH4 emissions
g/d 307.9 309.4 15.35 0.9350 0.2848 0.5405 g/kg of milk yield 18.8 18.3 1.48 0.6599 0.7323 0.4390
g/kg of fat yield 543.2 516.7 39.15 0.5750 0.9411 0.6236
g/kg of protein yield 585.7 543.8 39.98 0.3070 0.6489 0.2556
g/kg of milk solids yield 154.4 148.8 11.50 0.6313 0.9790 0.3510
g/kg of DMI 17.3 18.5 1.23 0.3380 0.1543 0.8623 1Standard error of the mean 2Treatment effect 3Sampling period effect
89
5.4. Discussion
Sward structure is defined as the distribution and arrangement of above-ground plant-part
components (Laca and Lemaire, 2000). In tropical grasses, characteristics such as pre- and post-
grazing SSH and leaf-to-stem ratio of the pre-grazing herbage mass play, along with herbage
allowance, an important role in determining herbage intake and animal performance (Da Silva and
Carvalho, 2005; Carvalho, 2013; Congio et al., 2018). In the present study, sward structure
characteristics and herbage allowance were similar for both treatments, excluding their effects on other
evaluated responses.
Diurnal variations in herbage chemical composition are directly related to NFC accumulation
as result of the balance between leaf photosynthesis and plant transpiration (Griggs et al., 2005;
Gregorini, 2012; Vibart et al., 2017). In the present study, DM concentration increased by 18% from
AM to PM herbage. Orr et al. (1997) reported increases of 57.3% and 44.4% for perennial ryegrass
(Lolium perenne L.) and white clover (Trifolium repens L.), respectively. However, most literature
reported increases from 14 up to 27% (Ciavarella et al., 2000; Delagarde et al., 2000; Trevaskis et al.,
2001; Gregorini et al., 2008; Abrahamse et al., 2009; De Oliveira et al., 2014; Pulido et al., 2015;
Vibart et al., 2017). Diurnal changes in temperature, solar radiation, and relative humidity, coupled
with accumulation of photosynthates explain the DM concentration from the morning to the afternoon
period (Gregorini et al., 2009).
Several studies described the pattern of NFC accumulation during the day, mostly on
temperate swards (Lechtenberg et al., 1971; Orr et al., 1997; Ciavarella et al., 2000; Griggs et al.,
2005; Gregorini et al., 2006; Shewmaker et al., 2006; Gregorini et al., 2008; Morin et al., 2011).
Greatest concentrations of SC and starch in plants growing in temperate regions were reported
between 12-13h after sunrise (Lechtenberg et al., 1971; Morin et al., 2011; Morin et al., 2012; De
Oliveira et al., 2018). In our study, afternoon herbage samples were taken approximately 10 h after
sunrise (4 pm), with increases of 52% in SC and 93% in starch for PM herbage compared to AM
herbage. Greater increases of SC were found in tropical grasses (mean of 68%; Trevaskis et al., 2001;
Fisher et al., 2005; De Oliveira et al., 2014). For temperate swards, including grass and legumes,
Pelletier et al. (2010) reported increases of SC from 6 to 105% for PM herbage compared to AM
herbage; however, most results reported mean increases of around 50% (Ciavarella et al., 2000;
Mayland et al., 2000; Pelletier et al., 2010; Vasta et al., 2012; Pulido et al., 2015; Vibart et al., 2017).
Increases in starch have been reported around 100% for PM temperate forage legumes (Orr et al.,
1997; Brito et al., 2008; Pelletier et al., 2010; Andueza et al., 2012) and 30% for PM temperate forage
grasses (Orr et al., 1997; Bertrand et al., 2008; Pelletier et al., 2010; Brito et al., 2016).
The increase in NFC and DM during the day dilutes other nutritional entities such as NDF,
ADF and CP (Gregorini, 2012; Vibart et al., 2017). In the present study, PM herbage had decreased
NDF (-2.9%) and ADF (-6.3%) relative to AM herbage but there was no effect in CP. Burns et al.
90
(2007) reported decrease of 7.4 and 6.7% for NDF and ADF, respectively, and no effect in CP
concentration for PM alfalfa (Medicago sativa L.). Similar results were found in perennial ryegrass by
Orr et al. (2001) and Abrahamse et al. (2009). Considering CP and N fractions, studies reported a
decrease on PM compared to AM herbage (De Oliveira et al., 2014; Pulido et al., 2015; Vibart et al.,
2017) while others showed no effect (Delagarde et al., 2000; Fisher et al., 2002; Gregorini et al.,
2008). In fact, greater concentrations of SC and starch in the afternoon improve the NFC/PROT ratio
which would optimize the supply of energy and protein to rumen microorganisms (Bryant et al., 2012;
Bryant et al., 2014) reducing urinary-N excretion and losses onto pastures (Gregorini et al., 2010;
Gregorini, 2012; Vibart et al., 2017).
The sampling period effect observed for some herbage chemical composition parameters
might be explained by the post-grazing SSH. During P2, post-grazing SSH was 3.6 cm lower than
during P1, which resulted in slightly greater proportion of stems on the pre-grazing herbage mass
(1.9% for P2 and 0.6% for P1; P = 0.037). Stems contain greater proportion of cell wall and less
photosynthetic tissues than leaves (Wilson and Kennedy, 1996) which explains the greater DM, NDF,
NDICP, and lower digestible protein reported during the P2. On the other hand, greatest lipid content
in plants is found within the chloroplasts (Harwood, 1980), most present in leaves relative to stems.
Daily herbage intake was similar between treatments, which is in agreement with previously
reported findings for grazing dairy cows (Gibb et al., 1998; Orr et al., 2001; Abrahamse et al., 2009;
Mattiauda et al., 2013; Pulido et al., 2015; Vibart et al., 2017). On the other hand, studies that
compared AM and PM herbage for housed ruminants reported greater DMI for animals fed with
feedstuffs harvested at sundown (Fisher et al., 1999; Burns et al., 2007; Pagano et al., 2011; Andueza
et al., 2012; Brito et al., 2008; 2009; 2016). Herbage DMI of grazing animals is a complex process
strongly influenced by non-nutritional or behavioral factors such as sward structure and foraging
behavior, whilst for housed animals herbage chemical composition and digestibility seem to be more
relevant in setting DMI (Poppi et al., 1987; Hodgson, 1990; Da Silva and Carvalho, 2005; Carvalho,
2013).
Milk yield from dairy cows grazing new paddocks allocated either in the morning or in the
afternoon showed only trends rather than significant treatment effects. Orr et al. (2001), although
noticing a trend (P = 0.076) of 5% increase in milk yield for PM cows over 4 experimental weeks,
concluded that there was no effect during the entire experimental period. Abrahamse et al. (2009)
reported significant increase (P < 0.05) in fat and protein corrected milk yield and fat yield, even
though no differences in milk yield were observed. Mattiauda et al. (2013), restricting grazing time to
4 hours of both periods of paddock allocation, observed a significant increase (P < 0.05) in protein
yield for PM cows. Pulido et al. (2015) reported no differences in milk and components yields.
Recently, Vibart et al. (2017) reported trends (P < 0.10) of greater fat, protein, and milk solids yield
for PM cows. In this study trends for greater protein (P = 0.0899) and casein (P = 0.0632) yields were
detected for cows grazing PM herbage. Brito et al. (2016) explained that greater proportion of
91
supplements may dilute the effect of high NFC of PM herbage. In the present study, concentrate meals
represented on average 33% of total DMI, greater than the amounts used by Vibart et al. (2017) (no
concentrate), Orr et al. (2001) (22%), and Abrahamse et al. (2009) (17%), and lower than Mattiauda et
al. (2013) (62%) and Pulido et al. (2015) (43%).
Indoor studies have shown that herbage intake of more balanced fermentable carbon to
nitrogen ratio from PM herbage can improve N utilization of dairy cows (Brito et al., 2008; 2009;
2016). The authors reported lower N intake, urinary-N concentration, N excretion, and more N
partitioning, with greater milk and protein yields for cows eating PM herbage. They also reported
lower MUN for PM cows indicating that an improved balance in the supply of energy from NFC and
N can reduce the excretion of urea N in milk. For grazing dairy cows, Vibart et al. (2017) observed
trends of greater N use efficiency with moderate increases in N captured towards milk. In this
experiment the higher NFC/PROT ratio in the PM herbage reduced the excretion of urea in milk and
increased N into protein and casein yield. This simple and non-cost grazing management strategy can
be an useful tool to improve N efficiency use of dairy cows and reduce N environment footprint in
dairy farming systems.
Enteric CH4 is influenced by the amount and nature of feed ingested by ruminants (Janssen,
2010). In this study, although timing of new paddock allocation has markedly affected herbage
chemical composition, it did not affect DMI. Factors that increase passage rate (i.e. higher nutritive
value and DMI) decrease CH4 formation per unit of feed eaten (Blaxter and Clapperton, 1965; Janssen,
2010; Hammond et al., 2013). The model proposed by Janssen (2010) suggests that greater passage
rates increase hydrogen concentration in the rumen making microorganisms select pathways that
produce less hydrogen, resulting in less CH4/kg of DM ingested. However, Hammond et al. (2013)
reported that 0.85 and 0.87 of total variation in daily enteric CH4 emissions of grazing sheep were
predicted by DM and OM intakes, respectively; while herbage chemical composition showed weak
correlation with both daily CH4 emission and CH4 yield (g/kg DMI). Although Ellis et al. (2012), in a
modeling exercise, showed the possibility to reduce enteric CH4 emission intensity of dairy cows fed
with high-sugar grasses, it was not confirmed by the results from this field experiment.
5.5. Conclusions
The results ratify the general understanding of diurnal variation in herbage chemical
composition towards greater concentrations of NFC and DM, and lower concentration of fiber
components in the afternoon herbage. However, the increase in nutritive value of the afternoon relative
to the morning herbage was not enough to increase DMI and milk yield, or to decrease CH4 emission
intensity by the dairy cows as hypothesized. The findings also indicate that new paddock allocation
92
during the afternoon can be a simple and useful grazing strategy that results in greater N partitioning
to protein yield, and lower excretion of urea N in milk.
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101
6. GENERAL CONSIDERATIONS
At the present time, the expected demand for food places farming systems under pressure
(Chiavegato et al., 2018), but the increase in agricultural outputs has to be coupled with the decrease in
environmental footprint (Godfray et al., 2010; Foley et al., 2011). In developing countries, agricultural
production must increase 80% through higher yields resulting from intensification of existing
agricultural systems (Conforti, 2011). In this sense, the concept of sustainable intensification began to
be addressed in such systems as a means of achieving higher yields through practices that decrease the
impact of key environmental issues (Royal Society, 2009; Garnett and Godfray, 2012).
Dairy farming systems from temperate pastures are more intensive than those from tropical
pastures (Congio et al., 2018) and their intensification is usually associated with more inputs of
nitrogen, to boost forage growth, or external supplementary feed, both aiming at increasing stocking
rate and productivity (Ramsbottom et al., 2015; Macdonald et al., 2017). In the tropics, dairy farming
systems besides having low N inputs, usually adopt inadequate grazing management strategies
resulting in low levels of milk productivity. Therefore, the sustainable intensification of tropical
pasture-based dairy systems may be possible through adoption of adequate grazing strategies rather
than extra nitrogen inputs or additional supplementary feed (Congio et al., 2018), provided that
minimum levels of soil fertility are provided to meet plant nutritional requirements.
This study was based in the literature that described the growth pattern of tropical forage
grass species under grazing (Carnevalli et al., 2006; Barbosa et al., 2007; Trindade et al., 2007; Da
Silva et al., 2009; Difante et al., 2009; Giacomoni et al., 2009; Barbosa et al., 2011; Gimenes et al.,
2011; Zanini et al., 2012; Silveira et al., 2013; Geremia et al., 2014; Pereira et al., 2014; Pereira et al.,
2015a; Pereira et al., 2015b; Silveira et al., 2016; Da Silva et al., 2017; Pereira et al., 2018; Sbrissia et
al., 2018). In general, there is a change in plant growth and pattern of herbage accumulation during
regrowth after reaching the canopy critical leaf area index (i.e. LI95%), when stem elongation and dead
material accumulation increase at the expense of leaf accumulation. Further, it has been systematically
observed that there is a positive relationship between canopy light interception and sward surface
height (SSH), indicating that SSH may be used as a reliable field index for monitoring and controlling
herbage regrowth (Da Silva et al., 2015). The results from this study corroborated the greater leaf
accumulation, herbage nutritive value, greater grazing efficiency, better tussock distribution, and
lower grazing losses on swards managed with the LI95% relative to the LIMax pre-grazing target. The
results from this study also integrated animal with plant responses and showed that the pattern of plant
growth during regrowth when managed with the LI95% target provides benefits to grazing animals and
to the system such as greater dry matter intake, higher milk yield and stocking rate resulting in 51%
increase in milk productivity. Additionally, benefits regarding issues of environmental concern were
also associated with this grazing management strategy, and corresponded to mitigation of emissions of
102
the two most representative greenhouse gases (GHG) in dairy farming systems per kg of milk
produced (i.e. CH4 and N2O/kg of milk).
Once the ideal pre-grazing management target was established during the first experiment,
the second experiment aimed at seeking the possibility of refinement by studying the ideal time of the
day to move animals to a new paddock. The results indicated that allocation of a new paddock at the
right pre-grazing condition during the afternoon provides herbage with a more balanced NFC/PROT
ratio to dairy cows, resulting in improved balance of protein and energy supply, and favoring
increased N retention through enhanced milk protein yield and less N as milk urea nitrogen. The
association of the LI95% pre-grazing target and PM allocation could bring economic, productivity and
environmental benefits towards sustainable intensification of tropical pasture-based systems. Both
findings highlight the opportunity to improve the efficiency of tropical pasture-based dairy systems
through practices that decrease the impact of key environmental issues, in accordance with the
principles of sustainable intensification.
Recently, land-based research institutes have been concentrating efforts in assessing sources
of GHG emissions in a broad range of agricultural systems around the world in order to generate field
data that can support accurate carbon footprint reports (Muñoz et al., 2016; Nascimento et al., 2016;
Luo et al., 2018; Pontes et al., 2018). Particularly, there are few data available regarding GHG
emissions in tropical regions, where most studies estimate carbon footprint from agricultural systems
using IPCC data (Intergovernmental Panel on Climate Change), with results that may be not so
accurate (Lessa et a., 2014; Cunha et al., 2016). Therefore, in loco studies are mandatory to support
accurate carbon footprint reports in tropical climate regions. Other aspect that impairs determination of
the real carbon footprint from agricultural systems is that most efforts are towards measuring sources
of GHG and few to evaluate carbon sequestration and storage in the soil. Perennial tropical pastures on
moist-warm climate have an enormous potential to increase soil organic carbon and offset GHG
emissions from livestock pasture-based systems (Braz et al., 2013; Abdalla et al., 2018). Abdalla et al.
(2018) highlighted that C4 grass species under high grazing intensities in moist-warm regions are more
likely to increase soil organic carbon than C4 under low grazing intensity. Therefore, further research
should focus on the analysis of carbon sequestration and stock in the soil, to achieve a more accurate
estimate of carbon balance and, therefore, to encourage mitigation strategies and programs by
producers in association with companies and policy makers.
103
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107
7. CONCLUSIONS
Strategic grazing management represented by the LI95% pre-grazing target associated with
moderate severity of defoliation (50% of the pre-grazing sward surface height) is an environmentally
friendly practice that improves the use efficiency of allocated resources through optimization of
processes involving plant, ruminant and their interface, and enhances milk production efficiency of
tropical pasture-based systems. In addition, daily allocation of herd to new paddock in the afternoon
might increase N partitioning to protein yield, and decrease excretion of urea N in milk. The
association of LI95% pre-grazing target and afternoon allocation could bring economic, productive and
environmental benefits towards sustainable intensification of tropical pasture-based systems.
